self-organized heterogeneous advanced … deliverable d2.2... · scenarios, kpis and evaluation...

Public distribution page 1 of 115

SHARING

SELF-ORGANIZED HETEROGENEOUS ADVANCED RADIO NETWORKS

GENERATION

D2.2

Scenarios, KPIs and Evaluation Methodology

for Advanced Cellular Systems

Date of delivery 31/01/2014

Contractual date of delivery 31/01/2014

Project number C2012/1-8

Editor(s) Frederic Pujol (IDATE)

Author(s) Frederic Pujol (IDATE), Berna Sayrac (FT), Yasir KHAN (FT), Sandeep Kottath (FT/EUR), David Bernard (FT), Thierry Clessienne (FT), Sofia MARTINEZ LOPEZ (FT), Jean-Marc Conrat (FT), Kimmo Hiltunen (ERICSSON), Jussi Turkka (MAGISTER), Yolanda Fernandez (TTI), Mathieu Brau (SIRADEL), Yoann Corre (SIRADEL), Mehdi Bennis (UOULU), Philippe Delahaye (NTUK), Mourad Khanfouci (MERCE), Hans Ahnlund (ECE), Serdar Sezginer (SEQ), Sylvie Mayrargue (CEA), Antonio Cipriano (TCS), Engin Zeydan (AVEA), Florian Kaltenberger (EUR), Raymond Knopp (EUR), Mohamad Assaad (SUP)

Dissemination level PU

Workpackage 2

Version 0.27

Total number of pages 115

Celtic-Plus SHARING Document D.2.2


Abstract:

Keywords: Scenarios, KPIs, evaluation methodology

This document defines the project scenarios, identifies the relevant KPIs and defines the evaluation methodologies to be used by the innovation WPs of the project (WP3, WP4 and WP5).

Nine deployment scenarios for future radio access networks have been defined and will be addressed in the project : 1-LTE macro cell only, 2-LTE small cell only, 3-LTE HetNet (LTE macro + LTE micro/pico/femto), 4-Inter-RAT HetNet (LTE + WiFi), 5-Relays, 6-Device-to-Device (D2D), 7-Carrier Aggregation (CA), 8-HSPA, 9-Generic multi-cell/Technology agnostic. Each deployment scenario contains several use cases whose specific characteristics have been presented in detail. An extensive KPI analysis has been made and a set of relevant KPIs as well as evaluation methodologies have been identified and described for each use case.

The deliverable will serve as a guideline for the technical work which will be carried out in the innovation WPs of SHARING.

Celtic-Plus Project Description

Confidential page 3 of 115

Document Revision History

Version Date Author Summary of main changes

0.1 20/9/2013 Frederic Pujol (IDATE) Creation of the Table of Contents (ToC)

0.2 14/10/2013 Berna Sayrac (FT) Comments on the ToC

0.3 29/11/2013 Berna Sayrac (FT) Preliminary input on scenarios FT

0.4 03/12/2013 Jussi Turkka (MAGISTER) Preliminary input on scenarios MAGISTER

0.5 03/12/2013 Jussi Turkka (MAGISTER) Further input on scenarios MAGISTER

0.6 05/12/2013 Yolanda Fernandez (TTI) Input on scenarios TTI

0.7 05/12/2013 Mathieu Brau (SIRADEL) Input on scenarios SIRADEL

0.8 13/12/2013 Frederic Pujol (IDATE) Integration of inputs from EUR, TCS and ERICSSON

0.9 16/12/2013 Mehdi Bennis (UOULU) Input on scenarios UOULU

0.10 18/12/2013 Frederic Pujol (IDATE) Inputs on executive summary, introduction and definition of KPIs

0.11 19/12/2013 Philippe Delahaye (NTUK) Input on D2D scenario description

0.12 19/12/2013 Mourad Khanfouci (MERCE)

Input on scenarios MERCE

0.13 19/12/2013 Hans Ahnlund (ECE) Input on scenarios ECE

0.14 19/12/2013 Serdar Sezginer (SEQ) Input on scenario SEQ and addition of KPIs

0.15 20/12/2013 Frederic Pujol (IDATE) Integration of an additional scenarios from FT and SUP

0.16 02/01/2014 Frederic Pujol (IDATE) Modifications in section 2

0.17-0.18 03/01/2014 Frederic Pujol (IDATE) Editorial corrections ad additions in sections 3 and 4

0.19-0.20 8/01/2014 Sylvie Mayrargue (CEA) Input on scenarios CEA

0.21 08/01/2014 Jean-Marc Conrat (FT) Input on scenario FT on relay propagation path-loss

0.22 09/01/2014 Mourad Khanfouci (MERCE)

Final modification of MERCE contribution

0.23 20/01/2014 Frederic Pujol (IDATE) Modifications following review process

0.24 22/01/2014 Yolanda Fernandez (TTI) Final modifications in TTI scenarios

0.25 27/01/2014 Frederic Pujol (IDATE) Integration of modifications following the review process

0.26 28/01/2014 Frederic Pujol (IDATE) Integration of modifications in KPI section

0.27

Final version 31/12/2014 Berna Sayrac (FT) Final review of the document



TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 7

1 INTRODUCTION ............................................................................................................. 8

2 DEFINITION OF PROJECT SCENARIOS ............................................................................ 9

2.1 DEPLOYMENT SCENARIO 1: LTE MACRO CELL ONLY ..................................................................... 11 2.1.1 Mobility Load Balancing (MLB) in LTE macro cell networks ......................................... 11 2.1.2 Load Balancing (LB) via transmit power optimization in LTE macro cell networks ........... 13 2.1.3 Capacity optimization through Active Antenna Systems (AAS) in LTE macro cell networks 15 2.1.4 Cross-layer performance evaluation of multi-cell cooperative schemes in LTE macro cell networks ....................................................................................................................... 17 2.1.5 Interference reduction in LTE through link level processing ........................................ 19 2.1.6 Multipoint Coordination Schemes for LTE-Advanced Networks ..................................... 20 2.1.7 Joint Processing CoMP algorithm, combined with interference cancellation at the receiver 21

2.2 DEPLOYMENT SCENARIO 2: LTE SMALL CELLS ONLY .................................................................... 22 2.2.1 Dynamic UL/DL Duplexing for TDD-enabled Small Cells ............................................. 22 2.2.2 ON/OFF power saving in campus of small cells ......................................................... 23

2.3 DEPLOYMENT SCENARIO 3: LTE HETNET (LTE MACRO+LTE MICRO/PICO/FEMTO) ................................. 25 2.3.1 Enhanced Inter-Cell Interference Co-ordination (eICIC) for interference management in

LTE-A HetNets ................................................................................................................ 25 2.3.2 Antenna tilt optimization for interference management in LTE-A HetNets ...................... 27 2.3.3 HetNet energy saving via eNodeB sleep mode/cell switch-off ...................................... 29 2.3.4 Dynamic cell ON/OFF power saving ........................................................................ 34 2.3.5 HetNet mobility .................................................................................................. 36 2.3.6 LTE-A multi-layer network in urban/suburban environments ....................................... 38 2.3.7 LTE-A multi-layer network in urban/suburban environments considering non-ideal backhaul

40 2.3.8 Opportunistic ON/OFF Switching for small cells ........................................................ 41 2.3.9 Backhaul offloading via opportunistic caching .......................................................... 42 2.3.10 Power saving through sum power optimization in HetNet deployments ...................... 43 2.3.11 Antenna Smart Grid Solutions for Outdoor DAS (Distributed Antenna Systems) .......... 45 2.3.12 Joint interference and location prediction ............................................................. 46 2.3.13 Performance and energy efficiency evaluation in LTE Heterogeneous Networks ........... 47

2.4 DEPLOYMENT SCENARIO 4: INTER-RAT HETNET (LTE + WIFI) ..................................................... 48 2.4.1 Multi-RAT Heterogeneous networks in urban/suburban environments .......................... 48 2.4.2 LTE-WiFi Offloading ............................................................................................. 49 2.4.3 Seamless Offloading in Heterogeneous Wireless Networks ......................................... 50 2.4.4 Positioning in Heterogeneous Converged Networks ................................................... 52

2.5 DEPLOYMENT SCENARIO 5: RELAYS ...................................................................................... 54 2.5.1 LTE-Advanced collaborative relaying ...................................................................... 54 2.5.2 Clustered wireless mesh networks based on LTE ...................................................... 56 2.5.3 Joint channel and network coding for advanced networks .......................................... 59 2.5.4 Path loss model for relay scenario .......................................................................... 61

2.6 DEPLOYMENT SCENARIO 6: DEVICE-TO-DEVICE (D2D) ............................................................... 63 2.6.1 D2D Unicast communications ................................................................................ 65 2.6.2 D2D broadcast communications ............................................................................ 66 2.6.3 D2D communications for overloaded networks ......................................................... 68

2.7 DEPLOYMENT SCENARIO 7: CARRIER AGGREGATION (CA) ............................................................. 69 2.7.1 Carrier aggregation using reconfigurable RF front-ends ............................................. 69 2.7.2 Antenna Design: small antenna for femto-cell, compatible with 4G systems bandwidths . 71 2.7.3 Coordinated carrier aggregation in campus of femto base stations............................... 72 2.7.4 Multi-Flow Carrier Aggregation .............................................................................. 74

2.8 DEPLOYMENT SCENARIO 8: HSPA ....................................................................................... 75 2.8.1 Macro densification for system capacity/performance ................................................ 75 2.8.2 Deployment optimization of Heterogeneous networks in urban/suburban environments .. 76

2.9 DEPLOYMENT SCENARIO 9: GENERIC MULTI-CELL/TECHNOLOGY AGNOSTIC .......................................... 78 2.9.1 Conversion block ................................................................................................ 78 2.9.2 Energy efficiency strategies over different power levels in the power amplifier ............... 80 2.9.3 Extension of Performance Evolution for Femto -> Small Cells ..................................... 81 2.9.4 Secure Communication with Imperfect CSIT ............................................................ 82 2.9.5 Flexible interference management schemes for downlink communications .................... 83


Public distribution 5 (115)

2.9.6 Theoretical Performance Analysis of Heterogeneous Cellular Networks .......................... 84 2.9.7 Relay-Aided Interference Mitigation ........................................................................ 85 2.9.8 Asymptotic Performance of HetNets under Dynamic Traffic ......................................... 86 2.9.9 Theoretical analysis for linearity and power efficiency trade-off in transmitters base stations

87 2.9.10 Distributed RRM Strategies without information exchange between the transmitters .... 88 2.9.11 Joint processing CoMP with limited backhaul and spatial CSIT allocation design ........... 89 2.9.12 Interference alignment for large dense network ..................................................... 90 2.9.13 Broadcast Channel Feedback in Cooperated Multiple Antenna Systems ...................... 91

3 DEFINITION OF KPIS ................................................................................................... 92

3.1 KPIS STATE-OF-THE-ART .................................................................................................. 92 3.1.1 System-level KPIs ............................................................................................... 93 3.1.2 Link level KPIs .................................................................................................... 95 3.1.3 Field trials KPIs ................................................................................................... 95 3.1.4 Misalignments and issues ...................................................................................... 95

3.2 SELECTION OF KPIS FOR SHARING ..................................................................................... 98

4 DEFINITION OF THE EVALUATION METHODOLOGY ..................................................... 102

5 CONCLUSIONS ........................................................................................................... 106

6 LIST OF ABBREVIATIONS, ACRONYMS, AND DEFINITIONS .......................................... 107

7 ANNEX: SCENARIOS/PARTNERS ................................................................................. 112

8 REFERENCES ............................................................................................................. 114



List of figures

Figure 1: MLB scenario with red arrows indicating outgoing handovers 11

Figure 2: Transmission power based load balancing scenario (G2 hotspot zone) 13

Figure 3: AAS based optimization with the optimization zone (T) and the observation zone (O) 15

Figure 4: User mobility within a macro cell CoMP scheme 17

Figure 5: Campus of small cells in ON/OFF power saving scenario 23

Figure 6: eICIC based interference management for LTE-A HetNets with pico cells 25

Figure 7: AAS based interference management with pico cells and optimization region 27

Figure 8: Distribution of the different types of areas and buildings. Commercial buildings are marked as blue, residential as red, and mixed (both commercial and residential floors) as magenta. Outdoor areas are marked as green. 31

Figure 9: Locations of the outdoor pico sites, as indicated by the black triangles. 31

Figure 10: Assumed residential, commercial and outdoor area traffic volume as a function of time. 31

Figure 11: Dynamic cell ON/OFF power saving 34

Figure 12: Example deployment scenarios: (a)Macro+outdoor small-cells, (b)Indoor small-cells. 38

Figure 13: Shopping mall HetNet deployment 43

Figure 14: IEEE 802.21 and user experience based offload scenario 50

Figure 15: Simulation scenario of converged heterogeneous small cell network 52

Figure 16: Multi-Relay Collaboration Scenario 54

Figure 17: Relay-aided Broadcast 54

Figure 18: Clusterized wireless mesh network 56

Figure 19: Multi-access relay-channel model 59

Figure 20: Two-users and two-relays communication system 60

Figure 21: Propagation channel environments for relay scenario 61

Figure 22: LTE D2D scenarios Within Network Coverage 63

Figure 23: LTE D2D scenarios Outside Network Coverage 64

Figure 24: CA deployment scenario a) providing nearly the same coverage and b) improving cell edge throughput 69

Figure 25: CA deployment scenario for HetNets 70

Figure 26: Coordinated carrier aggregation 72

Figure 27: (a) System layout, (b) Conversion block details 79



EXECUTIVE SUMMARY

This deliverable D2.2 “Scenarios, KPIs and evaluation methodology for advanced cellular systems” presents the outcome of the following tasks in WP2:

• Task 2.1.1 - Scenarios and Market analysis (Task Leader: IDATE). The scenarios are part of D2.2 but the market analysis will be presented in deliverable D2.5

• Task 2.1.2 - KPIs and Evaluation Methodology (Task Leader: FT)

The contents of this deliverable will be used as a basis for further work in WP2 (market analysis, global project concepts etc.) as well as for WP3, WP4 and WP5.

The objective of this deliverable is three-fold: 1-it defines the project scenarios on radio access technologies of today and the near future, 2-it presents a thorough KPI analysis and identifies relevant KPIs for the scenarios, and 3-it describes the appropriate evaluation methodologies in assessing the performances of the project scenarios in terms of the identified KPIs.

Future internet should be characterized by an important volume of traffic and a huge disparity in throughput, latency, mobility requirements, influenced by the growth of cloud computing, internet of service and internet of things; but also the societal evolution toward social networking and sharing of any content; or new global challenges like the target for energy-efficiency. The project scenarios in this deliverable have been defined based on this vision.

Considering possible evolution paths and enhancements for future radio access networks, the following deployment scenarios have been defined:

1. Deployment scenario 1: LTE macro cell only

2. Deployment scenario 2: LTE small cell only

3. Deployment scenario 3: LTE HetNet (LTE macro + LTE micro/pico/femto)

4. Deployment scenario 4: Inter-RAT HetNet (LTE + WiFi)

5. Deployment scenario 5: Relays

6. Deployment scenario 6: Device-to-Device (D2D)

7. Deployment scenario 7: Carrier Aggregation (CA)

8. Deployment scenario 8: HSPA

9. Deployment scenario 9: Generic multi-cell/Technology agnostic

These 9 deployment scenarios represent various deployment alternatives with different features for LTE-A and evolutions beyond LTE-A. Each deployment scenario contains several scenarios and each scenario corresponds to an innovation which will be studied (designed and evaluated) by one or several project partners. The outcome of these studies within each deployment scenario is expected to yield the overall expected benefits and drawbacks of that deployment scenario in terms of the identified KPIs and through the evaluation methodologies presented in this deliverable. Thus, we will be able to: 1-draw some general conclusions on each deployment scenario, 2-compare the relative tendencies in terms of performance, capacity and complexity; and finally 3-come up with recommendations to the operators on the pros-and-cons of each deployment choice for future radio access networks.

In order to present the scenarios as uniform as possible, the scenarios within each deployment scenario have been described in detail with the same set of characteristics/parameters (where applicable) which are put in a tabular form. Relevant KPIs and the appropriate evaluation methodology for each scenario are also presented and summarized in this deliverable.



1 Introduction

This deliverable D2.2 “Scenarios, KPIs and evaluation methodology for advanced cellular systems” presents the project scenarios, the relevant KPIs and the associated evaluation methodologies as the outcome of the following tasks in WP2:

• Task 2.1.1 - Scenarios and Market analysis (Task Leader: IDATE). The scenarios are part of D2.2 but the market analysis will be presented in deliverable D2.5

• Task 2.1.2 - KPIs and Evaluation Methodology (Task Leader: FT)

The project scenarios are presented as a set of 9 deployment scenarios where a deployment scenario implies distinct network features and deployment characteristics. Each deployment scenario comprises of several scenarios and each scenario corresponds to an innovation which will be studied (designed and evaluated) by one or several project partners. Such a structure allows us to asses the overall expected benefits and drawbacks of every deployment scenario in terms of the identified KPIs and through the evaluation methodologies presented in this deliverable. Thus, we will be able to: 1-draw some general conclusions on each deployment scenario, 2-compare the relative tendencies in terms of performance, capacity and complexity; and finally 3-come up with recommendations to the operators on the pros-and-cons of each deployment choice for future radio access networks.

Objective and structure of the document

The objective of this deliverable is three-fold: 1-it defines the project scenarios on radio access technologies of today and the near future, 2-it presents a thorough KPI analysis and identifies relevant KPIs for the scenarios, and 3-it describes the appropriate evaluation methodologies in assessing the performances of the project scenarios in terms of the identified KPIs. The contents of this deliverable will be used as a basis for further work in WP2 (market analysis, global project concepts etc.) as well as for WP3, WP4 and WP5.

Considering possible evolution paths and enhancements for future radio access networks, the following deployment scenarios have been defined:










The deliverable is organized to give a detailed presentation of the above 9 deployment scenarios, followed by a thorough KPI analysis, and the description of the associated evaluation methodologies. For uniformity purposes, the scenarios are presented using a template which contains a relevant set of aspects/characteristics/parameters for all scenarios.



2 Definition of project scenarios

This section describes in detail the project scenarios. As mentioned above, the 9 main deployment scenarios contain individual scenarios, each of which will be studied by one or more partners. The 9 deployment scenarios will allow us to come up with conclusions/comparisons on different deployment strategies for (near) future networks, i.e. which deployment has advantages/disadvantages under which conditions and according to which KPIs.

In order to present the scenarios as uniform as possible, the scenarios within each deployment scenario have been described in detail with the same set of aspects/characteristics/parameters (where applicable) in a tabular form. Below, you can see the list of these aspects/characteristics/parameters together with their short descriptions:

- Scenario title: The name of the scenario.

- Task: The project task in which the associated innovation is found.

- Network topology: The types of the radio access nodes in the network, such as macro cell only (only high-power nodes), small cell only (only low power nodes), HetNet (overlay of macro cells and small cells), multi-cell (any type of BS, could be high- or low-power).

- Radio Access Technology (-ies): The specific radio access technology/technologies which are used in the scenario, such as: HSPA, LTE(-A), Wi-Fi (or WLAN) or co-existence of two or more of these RATs.

- Nature of small cells: What type(s) of small cells is/are present in the scenario (if there are any small cells)? Examples are LTE(-A) pico/micro/femto cells, Wi-Fi access points or the co-existence of two or more of these nodes.

- Environment: The type of generic environment in which the evaluations are performed, such as dense urban, urban, suburban, rural etc. Also, more specific environment types, such as shopping center, university campus, train station, offices, homes/flats etc. can be considered.

- Context: The context in which the evaluations are performed, such as outdoor, indoor, in-vehicle etc.

- Inter-site distance (ISD): If applicable, the distance between several different types of nodes (macro, small cell, relay, UE etc.)

- Frequency deployment strategy: If applicable, an indication whether different nodes use different or the same frequency band/channel/carrier frequency. The former is called as separate channel deployment or frequency reuse greater than 1 whereas the latter is known as co-channel deployment or frequency reuse 1). There is also the possibility of having different carriers combined in a Carrier Aggregation (CA) scheme.

- Frequency bands: If applicable, which specific frequency bands are used in the scenario? Examples are LTE licensed bands, HSPA licensed bands, Wi-Fi unlicensed bands (2.4G or 5G), TV whitespaces or combinations of these.

- Density of small cells: If applicable, what is the density of the small cells? Ex: Dense (more than 2 per macro) or sparse?

- Backhaul: If applicable, the assumptions on the capacity of the backhaul(s) used in the scenario. Can be ideal (no latency, infinite capacity) OR non-ideal backhaul (for ex. X2 interface). The considered backhauls are between different types of nodes involved in the scenario (macro cells, small cells, relays, core network nodes etc.).

- Propagation/channel model: Which type of channel model is used in performance evaluation of the scenario? Here, a vide possibility of choices are possible, depending on the type of performance evaluations. Typical examples are path-loss models (statistical, ray-tracing etc.), shadowing characteristics, fast fading etc.

- Mobility model: If applicable, which type of mobility is used in evaluating the performance of the scenario? Typical examples are percentages of users moving at typical speeds (nomad/static users, pedestrians at 3 km/hr, in-vehicle users at 50 km/hr etc.

- Traffic model: If applicable, which type of traffic model is used in scenario evaluations? Typically Poisson arrivals are assumed with a fixed/pre-determined arrival rate and a fixed average service time. Different parameters (arrival rate and service time) can be assumed for non-homogeneous traffic (background traffic with hot-spots). Besides, constant (stationary) or time-varying (non-stationary) traffic characteristics are also considered.



- Services: If relevant, the services considered in the scenario, such as voice (VoIP), data (file download, web browsing, audio/video streaming etc.). A mixture of different services can be defined with certain percentages for each type of service. Also, the QoS requirements for each type of service are also of concern.

- Number of transmit/receive antennas (for MIMO schemes): If the scenario uses multiple antenna schemes, what are the number of transmit and receive antennas used?

- KPIs, metrics involved: What are the metrics/KPIs which will be used to evaluate the scenario? Here, as in the case of propagation channel models, a wide list exists. The used metrics/KPIs depend on the type/method of performance evaluations used. For link-level (PHY) evaluations, typical examples are Sum Rate, Bit Error Rate, Frame Error Rate, average link throughput etc. For system-level evaluations, typical examples are Avg. Cell Throughput, Block Call Rate, File Transfer Time, Latency etc. The choices and types of KPIs are thoroughly elaborated in section 3 of this document.

- Description of the problem to be solved (target) and proposed method for solution: A short description of the objective of the scenario and the innovation involved. For ex. maximizing sum rate (small cells + macro cells) through cell cooperation (CoMP), minimizing total energy consumed by macro cells and small cells through intelligent cell switch ON/OFF schemes, maximizing carried network traffic through intelligent Wi-Fi offloading mechanisms, developing realistic propagation models for base station-to-relay channels through ray tracing methods etc. Detailed descriptions of the scenarios are given in section 2 of this deliverable.

- Evaluation method: Which type of evaluation method will be used for the scenario? As mentioned above, the evaluation methodology depends on the type/nature of the evaluation (link/PHY or system level) and determines the metrics/KPIs. For link-level (PHY) evaluations, link level simulations are performed. These are small scale Monte-Carlo simulations (in the order of 1 ms or even below) where only the PHY (sometimes MAC) layer protocols are simulated. For system-level evaluations, system-level simulations are performed. These are medium- to large-scale Monte Carlo simulations (in the order of at least 1s typically) where MAC and above protocols (user arrivals/departures, mobility etc.) are simulated. Apart from those, there are also analytical/theoretical evaluation methodologies. These types of methods provide evaluations from an Information Theoretic perspective (typically capacity evaluations), PHY perspective (error rates, outage probabilities) or flow-level analysis (based on Queuing Theory). In general, such type of theoretical evaluations yield upper/lower bounds on performance in terms of link-level (PHY) metrics/KPIs. More elaboration on evaluation methods is given in section 4 of this deliverable.

Note that:

1- When there is a NA in any one of the above fields, it means that the characteristic/aspect is not relevant, i.e. not used in the study. This is often the case for theoretical studies where the analysis is made for a generic radio access system, without a specific RAT, topology, environment etc.

2- The characteristics/aspects are given according to the results which will be provided within the technical work carried out in the other WPs. Although most of the of the techniques proposed within the scenarios are applicable to a variety of technologies, topologies, layouts, environments, context, mobility/traffic types, channel models etc., only those

with which performance evaluation results will be provided are considered in this document and depicted in the tables.

3- The possible links between each scenario and other WPs are also indicated at the bottom of the tables.



2.1 Deployment scenario 1: LTE macro cell only

Deployment scenario 1 gathers the following 7 scenarios involving only LTE macro cells:

1. Mobility Load Balancing (MLB) in LTE macro cell networks 2. Load Balancing (LB) via transmit power optimization in LTE macro cell networks 3. Capacity optimization through Active Antenna Systems (AAS) in LTE macro cell networks 4. Cross-layer performance evaluation of multi-cell cooperative schemes in LTE macro cell

networks 5. Interference reduction in LTE through link level processing 6. Multipoint Coordination Schemes for LTE-Advanced Networks 7. Joint Processing CoMP algorithm, combined with interference cancellation at the receiver

2.1.1 Mobility Load Balancing (MLB) in LTE macro cell networks

This scenario will evaluate the performance of user mobility parametric changes for load balancing in LTE macro cell networks. Intra-LTE MLB consists of tuning the outgoing Handover Margin (HM) parameter of the handover procedure as defined by event A3 for LTE1.

The scenario will consist of a loaded (hotspot) cell surrounded by a group of unloaded cells (cf. Figure 1) and a certain user mobility which will allow the users to move between the cells. Changes to HM will dictate if the mobile user handover will be preponed or postponed thereby affecting other Key Performance Indicators (KPIs) of the individual cells such as Block Call Rate (BCR), Drop Call Rate (DCR), and Throughput.

This scenario allows us to 1- Study the impact of HM on user mobility, 2- Evaluate KPIs as a function of HM. 3- Develop a HM self-optimization framework for load balancing using statistical learning techniques. A system level simulator using only downlink transmissions, which performs correlated snapshots of users arrival, establish communication to download a file, mobility and departure on call termination will be used.

Figure 1: MLB scenario with red arrows indicating outgoing handovers

t1

t2

t3

t4

t2

t5

t6

t7

t8

t9

t10

s2

s3

s4

HM(s,t)



The details of the scenario are summarized in the table below:

Scenario aspect Description

Title Mobility Load Balancing (MLB) in LTE macro cell networks

Task T4.1

Network topology Macro cells only

Radio Access Technology (-ies) LTE

Nature of small cells No small cells

Environment (Sub-)urban

Context Outdoor

Inter-site distance 500 meters (macro-to-macro)

Frequency deployment strategy Co-channel deployment*

Frequency bands LTE licensed bands

Density of small cells No small cells

Backhaul Ideal backhaul

Propagation/channel model Path loss: 128.1+37.6log10(R), R in Km (3GPP Model 1, TR 36.814)

Lognormal shadowing standard deviation: 6 dB

Mobility model 90% of users at 30 km/hr

10% of users static

Traffic model Poisson arrivals with arrival rate of 2.4 users/second and avg. service time of 3 seconds with a spatial traffic distribution of 50% users in Hotspot

Services Data: 100% of users FTP (file size = 8 MB)

Number of transmit/receive antennas (for MIMO schemes)

SISO

KPIs, metrics involved Block Call Rate, Drop Call Rate, Load, File Transfer Time, Cell Edge Throughput, Cell Center Throughput

Description of the problem to be solved (target) and proposed method for solution

Mobility load balancing:

1. Minimizing mean squared difference of cell loads to balance cell loads and minimize interference between neighboring cells.

2. Use of surrogate based iterative self-optimization procedures to achieve the above objectives.

Evaluation method System level simulations

Links with other work packages: none

* Co-channel deployment in macro configuration means single carrier



2.1.2 Load Balancing (LB) via transmit power optimization in LTE macro cell

networks

This scenario evaluates the performance of load balancing algorithm via transmit power optimization in LTE macro deployment scenario. So far, studies on optimization performance evaluation for load balancing solutions that are based on transmit power optimization have not been carried out.

Via this scenario, the impact of capacity in a well-covered network and the influence of transmit powers on network KPIs with the relative drawbacks/advantages will be studied extensively. The scenario involves a central hotspot zone (G1) surrounded by a regular traffic zone (G2) (c.f. Figure 2). The eNBs in the two zones can adjust their transmit powers to alter the extent of their coverage, thereby forcing users to get handed over between the neighboring cells and improving/degrading the Quality-of-Service in the respective regions. The second busiest hour data from a daily traffic pattern which is extracted from an operational network will be used for simulations.

The scenario will allow us to 1- understand the mutual interactions between the eNBs to reduce overall network inter-cell interference. 2- understand the impact of transmit power changes on network KPIs such as Load, Block call Rate (BCR), File Transfer Time (FTT), 3- develop and evaluate a transmit power based iterative self-optimization framework for load balancing and 4- evaluate and compare the performance of using the second-busiest-hour optimized transmit power settings to daily traffic pattern.

In order to achieve these goals, flow level system simulations will be used.

Figure 2: Transmission power based load balancing scenario (G2 hotspot zone)

G1

G2

eNB2

eNB3eNB1

eNB4

eNB5

eNB6





Title Load Balancing (LB) via transmit power optimization in LTE macro cell networks

Task T4.1




Environment (Sub-)Urban

Context Outdoor

Inter-site distance 900 meters macro to macro in zone G1, 300 meters macro to macro in zone G2

Frequency deployment strategy Co-channel deployment




Propagation/channel model Path loss: 128.1+37.6log10(R), R in Km (3GPP Model 1, TR 36.814)

Lognormal shadowing standard deviation: 6 dB

Mobility model No mobility

Traffic model Poisson arrivals with constant average arrival rate λ and constant average service time µ.

Services Data: 100% of users FTP (file size = 10 MB)


SISO

KPIs, metrics involved Block Call Rate, Load, File Transfer Time


Transmission power based load balancing:

1. Minimizing maximum of cell loads to balance cell loads and minimize interference between neighboring cells.


Evaluation method Flow level system simulations2




2.1.3 Capacity optimization through Active Antenna Systems (AAS) in LTE

macro cell networks

This scenario evaluates the performance of capacity improvements/changes by changing the antenna tilts of individual eNBs in LTE macro cell network actively i.e. wherein the elements of the antenna are electronically adjusted thereby varying the beam characteristics in terms of azimuth and/elevation. So far studies on the tilt based performance evaluation of capacity and/or coverage self-optimization has been carried out only using the Remote Electrical Tilts (RETs) of the passive antennas.

The scenario will involve a cluster of Target cells (c.f.Figure 3) with irregular traffic density and the tilts � of each of which can be varied to evaluate the effect on network KPIs. This will be surrounded by a group of Observation cells whose KPIs will be monitored along with the KPIs of the target cells.

The scenario allows us to 1- define a set of KPIs each of which will either jointly or independently provide insight into the cell-to-cell tilt based interactions 2- fully understand the effect of antenna tilt changes in target cells on the KPIs of the target and/or observation cells with respect to capacity/coverage and inter-cell interferences. 3-define and evaluate a generic iterative self-optimization framework based on stochastic approximation for optimizing any individual/joint KPI.

Simulations will be carried out for downlink only using a system level simulator without considering user mobility.

Figure 3: AAS based optimization with the optimization zone (T) and the observation zone (O)

T2

T1

T3

T4

T5

T7

O8

O9

O10

O11

O12

O13

O14

O15

O16

O17

O18

O19

T6





Title Capacity optimization through Active Antenna Systems (AAS) in LTE macro cell networks

Task T4.1 Network topology Macro cells only Radio Access Technology (-ies) LTE Nature of small cells No small cells

Environment (Sub-)urban Context Outdoor Inter-site distance 500 meters for macro-to-macro Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells No small cells Backhaul Ideal backhaul Propagation/channel model Macro to UE: 128.1+37.6log10(R[km])

Pico to UE: 140.7+36.7log10(R[km]) ]) (3GPP Model 1, TR 36.814) Lognormal shadowing standard deviation: 6 dB

Mobility model No mobility Traffic model Poisson arrivals with constant average arrival rate λ and

constant average service time µ.

Services Data: 100% of users FTP (file size = 10 MB) Number of transmit/receive antennas (for MIMO schemes)

SISO

KPIs, metrics involved Block Call Rate, Cell Edge Throughput, Cell Center Throughput, Load, File Transfer Time for both Target and Observational cells


Antenna tilt optimization for capacity enhancement: 1. Maximizing sum of cell edge throughput and sum of

cell center throughputs of target cells under constraints on Block Call Rate.

2. Minimizing the maximum of cell loads of target cells under constraints on Block Call Rate.

3. Minimizing the sum of File Transfer Times of all target cells under constraints on Block Call Rate.


Evaluation method System level simulations3




2.1.4 Cross-layer performance evaluation of multi-cell cooperative schemes in

LTE macro cell networks

This scenario evaluates the performance of most common multi-cell cooperation schemes (i.e. CoMP schemes) in LTE macro cell networks in a cross-layer manner, i.e. taking into account higher layer phenomena, particularly Radio Resource Control (RRC) or Radio Resource Management (RRM) processes such as user mobility. So far, studies on performance evaluation of the existing CoMP solutions have been done exclusively in a PHY/MAC context, using only PHY/MAC layer models and discarding the effects of RRC/RRM procedures. This situation prevents us to fully know the benefits/drawbacks of such solutions when deployed on real networks where User Equipment (UE) move and their mobility/conditions are impacted by RRM/RRC procedures (cf. Figure 4). Hence, this scenario allows us to: 1-fully understand the interactions between the PHY/MAC and RRC/RRM aspects in the existing CoMP solutions; and 2-have a more realistic performance evaluation of those solutions from a system level point of view. In order to achieve these goals, new cross-layer models and performance evaluation methods will be developed, verified by realistic system-level simulations.

Figure 4: User mobility within a macro cell CoMP scheme





Title Cross-layer performance evaluation of multi-cell cooperative schemes in LTE macro cell networks

Task T3.1 Network topology Macro cells only Radio Access Technology (-ies) LTE Nature of small cells No small cells

Environment Urban/Dense urban/Rural Context Outdoor Inter-site distance 500-2000 meters Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells No small cells Backhaul Ideal backhaul Propagation/channel model 3GPP channel models4 Mobility model Random motion of users with:

− a mix of varying speeds between 0-30 km/hr − 3GPP mobility settings5

Traffic model Elastic traffic of Poisson arrivals with constant average arrival rate λ and constant average service time µ.

Services Data service: file download of a fixed file size Number of transmit/receive antennas (for MIMO schemes)

SISO

KPIs, metrics involved Block Call Rate, Cell Loads, File Transfer Time, Throughput, Capacity.


Performance evaluation of the existing CoMP schemes taking into account RRM/RRC processes such as user mobility. Cross-layer model development based on queuing theory, validated by system-level simulations.

Evaluation method Cross-layer models based on queuing theory and system-level simulations.




2.1.5 Interference reduction in LTE through link level processing

This scenario will study PHY layer transmitter-side solutions on:

- interference suppression or avoidance with advanced MIMO schemes taking care of performance and implementation feasibility in single-cell multi-user configurations, and

- advanced interference mitigation in the uplink, with optimization of the reception and interference rejection techniques in the uplink, for single carrier waveform and frequency domain equalization.

The focus will be in open loop schemes for an improvement of the budget link and for mitigating interferences. The main investigation track will be on linear dispersive spatio-temporal codes, which generates transmitted signals with non-circular statistics. With this kind of signals, widely linear receiver schemes may be used to efficiently suppress or mitigate interference.



Title Interference reduction in LTE through link level processing

Task T3.1 and T3.2

Network topology Macro cell only

Radio Access Technology (-ies) LTE-A

Nature of small cells NA

Environment (Sub-)urban

Context Both Outdoor and Indoor

Inter-site distance Few kms for macro-to-macro



Density of small cells NA

Backhaul Ideal backhaul between macro cells

Propagation/channel model

Path loss: Winner 2D model

Shadowing: Winner 2D model

Fast fading: Rayleigh

Mobility model 100% of pedestrians users, or 100% of vehicular users

Traffic model Full buffer constant traffic for PHY simulations

Services NA


2x1 or 2x2 or 4x1 or 4x2

KPIs, metrics involved Bit Error Rate


Maximizing sum rate through interference mitigation techniques

Evaluation method Analytical

Link level simulations




2.1.6 Multipoint Coordination Schemes for LTE-Advanced Networks

This scenario considers the definition and performance evaluation of an efficient scheduling method for multipoint coordination schemes (CoMP) in LTE-A and beyond. CoMP requires close coordination between several eNBs. In a near future scenario, no-CoMP users will co-exist with CoMP users (i.e. users having the opportunity to use the CoMP feature). Therefore, the same eNB schedulers must manage CoMP as well as no-COMP users with fairness. This scenario will investigate fair scheduling schemes which handle this co-existence. The system performance will be evaluated by a system simulator and compared to a no-COMP system. We will also treat the impact of the backhaul imperfection and of the feedback latency on each proposed scheduler.


Scenario aspect Description Title Multipoint Coordination Schemes for LTE-Advanced

Networks Task T3.1




Environment Dense urban, Urban, Suburban and Rural

Context Outdoor

Inter-site distance 500, 1 732 and 1 299 m for macro-to-macro





Propagation/channel model Compliant to ITU-R M.2135-1

Mobility model 3,30, 90 and 120km/h as specified by ITU-R M.2135-1

Traffic model Full buffer and TUDR

Services Voice and Data: 100% of users FTP


MIMO

KPIs, metrics involved Avg. cell throughput


Maximizing sum rate (macro cells) through cell cooperation (CoMP)





2.1.7 Joint Processing CoMP algorithm, combined with interference cancellation

at the receiver

In this scenario, two adjacent cells with multi-antenna eNBs cooperate in a CoMP Joint Processing (JP) scheme, at least for cell edge users. A MU-MIMO scheme is used in at least one cell in order to compensate for the incurred loss of resource due to JP CoMP. However, whatever codebook may be used at the eNB, the UE will still experience some residual interference due to paired UEs of the MU-MIMO scheme. Therefore, a CoMP UE should be equipped with an interference cancellation receiver. The goal is to evaluate the performance of such a combined DL scheme: CoMP with MU-MIMO, combined with interference cancellation at the UE receiver.



Title JP CoMP algorithm, combined with interference cancellation at the receiver

Task T3.1 Network topology Macro cell only Radio Access Technology (-ies) LTE-A Nature of small cells NA Environment (Sub)-urban Context Outdoor Inter-site distance 500 or 1732 meters for macro-to-macro Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells NA Backhaul Ideal backhaul Propagation/channel model Spatial Channel Model Extended (SCME)

Mobility model Spatial Channel Model Extended (SCME) Traffic model NA Services NA Number of transmit/receive antennas (for MIMO schemes)

{2 or 4} / 2

KPIs, metrics involved Bit Error Rate Packet Error Rate


In a CoMP MU MIMO scenario, implement an interference cancellation scheme at the receiver

Evaluation method Link level simulations




2.2 Deployment scenario 2: LTE small cells only

Deployment scenario 2 gathers the following 2 scenarios involving only LTE small cells:

1. Dynamic UL/DL Duplexing for TDD-enabled Small Cells 2. ON/OFF power saving in campus of small cells

2.2.1 Dynamic UL/DL Duplexing for TDD-enabled Small Cells

This scenario considers the flexible and dynamic UL and DL switching in TDD-enabled small cells. Due to the traffic asymmetry in small cells, there is a need to optimize the switching point† locally taking into account various key parameters such as interference levels, file size, latency, and small cell density. Other interesting aspects related to the UL/DL switching point entail clustering among locally coupled small cells, power control and other interference management techniques.

The proposed algorithm is self-organizing such that decisions are taken locally at the small cells as a function of the traffic load and interference levels at both DL and UL.



Title Dynamic UL/DL duplexing for TDD-enabled small cells

Task T4.1

Network topology Outdoor pico

Radio Access Technology (-ies) LTE(-A)

Nature of small cells LTE pico

Environment Urban

Context Both outdoor and indoor areas (evaluation area)

Inter-site distance 500 m



Density of small cells Uniform and non-uniform hotspots

Backhaul X2

Propagation/channel model Short-term fading and Lognormal shadowing

No fast fading for any of the links

Mobility model Static users

Traffic model Full buffer and non-full buffer

Services Various QoS classes


1x1 (downlink)

KPIs, metrics involved Average throughput per user

DL and UL packet throughputs


Minimizing a cost function of packet delay, interference levels and traffic load

Evaluation method Analytical and system level simulations (MATLAB)


† A switching point is defined as the point in which a small cell BS switches from UL to DL mode

Celtic-Plus SHARING

Public distribution

2.2.2 ON/OFF power saving in campus of small cells

This scenario will evaluate the performance of ON/OFF power saving techniques wsmall cells (ES small cells) are semaximum power (compensating base stations).

Single and multiple cells compensation based ON/OFF energy saving is currently discussed in 3GPPtwo level network planning is considered as cells set. Primary network planning is considered for the full loaded network and another network planning is considered for lower UE traffic.

We will propose a SON inspired algorithm for finding thcampus) of base stations coordinated by a central node (management and group RRM for the campus

Figure 5: Campus of small cells in ON/OFF power saving scenario

ON/OFF power saving in campus of small cells

This scenario will evaluate the performance of ON/OFF power saving techniques where a chosen set of small cells (ES small cells) are set OFF while another set of small cells are kept transmitting at

base stations).

Single and multiple cells compensation based ON/OFF energy saving is currently discussed in 3GPPtwo level network planning is considered as a solution for the determination of the compensation small cells set. Primary network planning is considered for the full loaded network and another network planning is considered for lower UE traffic.

SON inspired algorithm for finding the compensation base stations in a group (campus) of base stations coordinated by a central node (called as campus GW) that will provide management and group RRM for the campus (Figure 5).

Campus of small cells in ON/OFF power saving scenario

Document D.2.2

23 (115)

ere a chosen set of t OFF while another set of small cells are kept transmitting at

Single and multiple cells compensation based ON/OFF energy saving is currently discussed in 3GPP6 and solution for the determination of the compensation small

cells set. Primary network planning is considered for the full loaded network and another network

e compensation base stations in a group (i.e. campus GW) that will provide



The parameters of the scenario are further described through the following table:


Title ON/OFF power saving in campus of small cells Task T4.3 Network topology Coordinated group of small cells ( campus) Radio Access Technology (-ies) LTE-A Nature of small cells LTE-A Pico Environment Dense urban Context Outdoor Inter-site distance Random deployment of the campus Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells Dense/highly dense campus Backhaul Ideal and non ideal backhaul for the small cells.

Propagation/channel model Path loss: outdoor pico model of 3GPP TR 36.8147 Shadowing: Fixed Fast fading: none

Mobility model Static to low mobility UEs

Traffic model Spatial Poisson distribution with higher intensity around the pico cells (as in 3GPP8).


SISO

KPIs, metrics involved Cell Edge Throughput, Cell Edge SINR, Cell Center Throughput, Cell Center SINR

Description of the problem to be solved (target)

SON techniques for finding the compensation small cells in the campus of the base stations.


Links with other work packages:

The scenario is used in T4.3 as baseline for the evaluation of the ON/OFF power saving. Large campuses are used in T4.4 for the evaluation of the distributed synchronization algorithms. The scenario have link with WP6 since it assume new coordination node (campus GW) that coordinate between radio resources management (RRM) actions in the campus additional signaling and/or interfaces are needed for such common RRM.



2.3 Deployment scenario 3: LTE HetNet (LTE macro+LTE micro/pico/femto)

Deployment scenario 3 gathers the following 13 scenarios involving LTE HetNet (LTE macro+LTE micro/pico/femto):

1. Enhanced Inter-Cell Interference Co-ordination (eICIC) for interference management in LTE-A HetNets

2. Antenna tilt optimization for interference management in LTE-A HetNets 3. HetNet energy saving via eNodeB sleep mode/cell switch-off 4. Dynamic cell ON/OFF power saving 5. HetNet mobility 6. LTE-A multi-layer network in urban/suburban environments 7. LTE-A multi-layer network in urban/suburban environments considering non-ideal backhaul 8. Opportunistic ON/OFF Switching for small cells 9. Backhaul offloading via Opportunistic caching 10. Power saving through sum power optimization in HetNet deployements 11. Antenna Smart Grid Solutions For Outdoor DAS 12. Joint interference and location prediction 13. Performance and energy efficiency evaluation in LTE Heterogeneous networks

2.3.1 Enhanced Inter-Cell Interference Co-ordination (eICIC) for interference

management in LTE-A HetNets

This scenario will evaluate the impact of 1-interference on network performance 2-self-optimization in co-channel deployment of macro and pico layers.

This scenario will study the impact of eICIC parameters on the network performance, namely: 1-pico cell Range Extension (biasing) and 2-Muting Ratio. Such a performance evaluation will enable us to study the impact of these parameters, their advantages/drawbacks especially for cell edge performances and will provide an insight into the optimum regions of operation in a given network.

More precisely the scenario will allow us to 1-study the effect of traffic absorption by the pico biasing on cell-edge/cell-center SINR and throughput, 2-study the effect of macro sub-frame blanking, its advantages for pico users in terms of cell edge quality and disadvantages for macro users in terms of call blockings, 3-define joint network KPIs for evaluating network performance in terms of changes in pico biasing and macro Muting Ratios and 4- propose a centralized iterative stochastic process based self-optimization mechanism for optimizing the network performance.

The scenario will consist of a central loaded base station deploying several pico cells to offload traffic (c.f. Figure 6). This will be surrounded by the first tier neighbors. A Monte-Carlo based system level LTE-A simulator for downlink will be used to replicate a real network.

Figure 6: eICIC based interference management for LTE-A HetNets with pico cells





Title Enhanced InterCell Interference Co-ordination (eICIC) for interference management in LTE-A HetNets

Task T4.1 Network topology HetNet (overlay of macro cells and small cells) Radio Access Technology (-ies) LTE-A Nature of small cells LTE-A Pico

Environment Dense urban Context Outdoor Inter-site distance 500 meters for macro-to-macro

250 meters for macro-to-pico 100 meters for pico-to-pico

Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells Dense, all of which are deployed only in the congested

macro cells Backhaul Ideal backhaul between small cells

Ideal backhaul between small and macro cells Ideal backhaul for all other interfaces

Propagation/channel model Macro to UE: 128.1+37.6log10(R[km]) Pico to UE: 140.7+36.7log10(R[km]) (3GPP Model 1, TR 36.814) Lognormal shadowing standard deviation: 6 dB

Mobility model 2/3 of users static and within small cells 1/3 of users mobile at 30 km/h and within macro cells

Traffic model Regular Poisson distribution based arrivals with higher intensity in Hotspot and regular intensity in all other areas


SISO

KPIs, metrics involved Block Call Rate, Cell Edge Throughput, Cell Edge SINR, Cell Center Throughput, Cell Center SINR,


eICIC optimization for interference management: 1. Minimizing a joint performance metric of cell edge and

cell center SINRs under constraints on Macro Block Call Rate.






2.3.2 Antenna tilt optimization for interference management in LTE-A HetNets

This scenario investigates the impact of Active Antenna System (AAS) based tilt optimization in a heterogeneous network (Figure 7). Although studies have been carried out to investigate the impact of antenna tilt changes for a regular macro network, none have been done so far for a HetNet scenario.

The interference impact of antenna tilts on low-power-nodes/Picos, the relative advantages in terms of total network capacity enhancements due to addition of Pico cells or degradations due to Macro induced interferences have not been addressed so far. Hence this scenario allows us to 1- define and evaluate Macro and Pico centric KPIs and the impact on them due to antenna tilts 2- understand the capacity-coverage tradeoffs due to antenna tilts; 3- develop tilt based self-optimization stochastic approximation methodology to optimize the network based on operator centric objectives which could be based on either or a combination of cell-edge/cell-center SINR/Throughputs.

In order to achieve this goal; AAS based antenna tilts to a central higher traffic cell containing several Pico cells surrounded by a first tier neighbor, will be carried out using a downlink system level simulator and optimization performance evaluated using the defined KPIs.

Figure 7: AAS based interference management with pico cells and optimization region





Title Antenna tilt optimization for interference management in LTE-A HetNets

Task T4.1 Network topology HetNet (overlay of macro cells and small cells) Radio Access Technology (-ies) LTE-A Nature of small cells LTE-A Pico

Environment Dense urban Context Outdoor Inter-site distance 500 meters for macro-to-macro

250 meters for macro-to-pico 100 meters for pico-to-pico

Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells Dense, all of which are present only in the most

congested central macro cell Backhaul Ideal backhaul between small cells

Ideal backhaul between small and macro cells Ideal backhaul for all other interfaces

Propagation/channel model Macro to UE: 128.1+37.6log10(R[km]) Pico to UE: 140.7+36.7log10(R[km]) (3GPP Model 1, TR 36.814) Lognormal shadowing standard deviation: 6 dB

Mobility model 2/3 of users static and within small cells 1/3 of users mobile at 30 km/h and within macro cells

Traffic model Poisson arrivals with constant average arrival rate λ and constant average service time µ.


SISO

KPIs, metrics involved Block Call Rate, Pico and Macro Cell Edge Throughput, Cell Edge SINR, Pico and Macro Cell Center Throughput, Cell Center SINR


Antenna tilt optimization for interference management: 1. Maximizing sum of cell edge throughput and cell

center throughputs under constraints on Block Call Rate.






2.3.3 HetNet energy saving via eNodeB sleep mode/cell switch-off

This scenario will be used to evaluate the performance of an energy saving scheme, where underutilized low-power cells are switched to sleep mode, or switched off completely, when they are not needed to serve close-by users. The network consists of a macro cell layer and a small cell layer operating on the same carrier frequency. The outdoor small cell layer is assumed to be planned in the sense that the small cells are deployed within the traffic clusters, where they are needed to provide the required area capacity during some part of the day. The main principle is that the network is able to serve the hourly offered traffic with an acceptable cell-edge user performance throughout the day.

The system simulations are assumed to be semi-dynamic, which means that the users are assumed to be static, but the user data traffic and the RRM is assumed to be time-dynamic, and modeled on the TTI (1 ms) level. New users are created in random and 3D network positions following the desired (hourly) distribution between residential, commercial and outdoor users.

Only downlink is modeled in detail. For uplink, a very simple model of the mobile station transmission powers is included, enabling the estimation of the received uplink power levels at the base stations.

A dynamic energy saving algorithm is assumed. If the pico cell utilization is judged to be sufficiently low (based on some criteria, e.g. cell throughput or cell resource utilization), the cell is switched to sleep mode, meaning that all downlink transmissions are discontinued. This also means that the users are not able to access a sleeping pico cell. If (based on some criteria/measurements) a sleeping pico cell would be needed to serve close-by users, the energy saving algorithm will initiate a cell re-activation process, after which the users will be able to access the pico cell.

In addition to the sleep mode, fast cell DTX can be applied to active, but idle cells. In case of fast cell DTX, part of the base station, typically the power amplifier is switched off in between the CRSs. In doing so, the base station power consumption can be reduced, while still keeping the cell accessible for the users.

Furthermore, in addition to the sleep mode, the underutilized pico cells can be switched off completely. While the power consumption will become lower compared to a sleep mode, the cell re-activation can take a longer time, which should be taken into account when designing the cell switch-off algorithm.

In case of base station power consumption models, input from previous projects, such as EARTH, will be utilized as much as possible.





Title HetNet energy saving via eNodeB sleep mode/cell switch-off

Task T4.3

Network topology HetNet (LTE macro + LTE outdoor pico)



Environment Urban, a mixture of residential and commercial areas/buildings and areas without buildings (parks, roads etc). An example is shown in Figure 8

Context Both outdoor and indoor users, only outdoor cells

Inter-site distance 500 m for macro cells

Irregular for the small cell layer (a planned deployment within traffic hotspots). An example is shown in Figure 9



Density of small cells Sparse to dense (irregular), see Figure 9

Backhaul Non-ideal (X2)

Propagation/channel model WINNER C2 (NLOS) for the macro-cellular propagation. Recursive street-level propagation for the micro-cellular propagation within the traffic cluster and within the street canyons. A more generic micro-cellular propagation for larger NLOS distances between bins, e.g. ITU-R P14119 “over-the-rooftop” model.

NLOS and LOS building penetration models as described in COST-2311011.

3D propagation for the macro-cellular propagation (floor height gain)

Car body loss for in-vehicular users.

3D eNodeB antennas both for macro (down-tilted 3-sector) and pico (omni-directional)

Lognormal shadowing with exponential spatial correlation for all links (standard deviation and the correlation distance will vary).

No fast fading for any of the links.

Mobility model Static users. 80% inside the modeled buildings, 20% outdoor or inside vehicles

Traffic model 3GPP FTP model 112 with a fixed packet size (e.g. equal to 500 kB).

Hourly offered area traffic will vary as a function of time. The fractions of residential, commercial and outdoor traffic will vary as a function of time. An example is shown in Figure 10

Services 100% data (FTP)


2x2 (downlink), 1x2 (uplink)

KPIs, metrics involved Average throughput for the worst 5th percentile of the users (system coverage)

Offered area traffic, served area traffic (system capacity)

Average cell utilization

Average cell sleep probability

Average cell switch-off probability

Total network power consumption

Daily network energy consumption (over 24 hours)


Minimizing total energy consumed by macro cells and small cells through intelligent sleep mode and/or cell switch ON/OFF schemes





Figure 8: Distribution of the different types of areas and buildings. Commercial buildings are marked as blue, residential as red, and mixed (both commercial and residential floors) as magenta. Outdoor areas

are marked as green.

Figure 9: Locations of the outdoor pico sites, as indicated by the black triangles.

Figure 10: Assumed residential, commercial and outdoor area traffic volume as a function of time.

-800 -600 -400 -200 0 200 400 600 800

-600

-400

-200

0

200

400

600

-800 -600 -400 -200 0 200 400 600 800

-600

-400

-200

0

200

400

600

0 5 10 15 200

20

40

60

80

100

120

140

160

180

200

Time [h]

Area Traffic [Mbps/km2]

Outdoor

Commercial

Residential



The KPIs which will be used for this scenario are detailed below:

1. System coverage is estimated by looking at the average throughput for the worst 5th percentile of the users. The average throughput is calculated by dividing the size of the transmitted packet with the time it took to transmit the packet.

2. System capacity is estimated by looking at the offered area traffic or the served area traffic, measured as Mbps/km2. Served area traffic is measured as the total number of successfully transmitted bits within all cells in the system divided by the total system area and the simulation time.

3. Average cell utilization is defined as the percentage of the time and frequency domain resources used within a cell during the simulation time. A separate utilization value will be calculated for each hour.

4. Average sleep mode probability is defined as the percentage of time a pico cell is in sleep mode. A separate value will be calculated for each hour. Similarly, if cell switch-off is applied, average switch-off probability will be calculated.

5. Total network power consumption is estimated by looking at the average network power consumption during each hour, i.e. when the average offered area traffic is assumed to be fixed, taking into account both the power consumed during the active and the idle periods of eNodeB operation. Total network power consumption (kW) is calculated as the total power consumed by all the operational macro and pico base stations within the system.

6. Daily network energy consumption (kWh) is calculated as the sum of the hourly network energy consumption values over a period of 24 hours.

Note that both the total network power consumption and the daily network energy consumption can be normalized by dividing the power and energy consumption values with the network area (kW/km2 or kWh/km2).

The assumed evaluation method is multi-cell system level simulations. The network consists of a macro cell layer and a small cell layer operating on the same carrier frequency. The outdoor small cell layer is assumed to be planned in the sense that the small cells are deployed within the traffic clusters, where they are needed to provide the required area capacity during some part of the day. The main principle is that the network is able to serve the hourly offered traffic with an acceptable cell-edge user performance throughout the day.

The system simulations are assumed to be semi-dynamic, which means that the users are assumed to be static, but the user data traffic and the RRM is assumed to be time-dynamic, and modeled on the TTI (1 ms) level. The traffic model is based on the 3GPP FTP model 1. Hence, a Poisson process is applied to model user arrivals utilizing the assumed hourly offered area traffic value as an input. New users are created in random and 3D network positions following the desired (hourly) distribution between residential, commercial and outdoor users.

After the user traffic has been created, a serving cell is assigned to it. The serving cell selection is assumed to be based on the received downlink signal strength (RSRP), taking the possible cell individual offset values into account, when applicable. Next, the user initiates a packet transmission, and when the packet has been successfully transmitted, the user leaves the system.

Only downlink is modeled in detail. For uplink, a very simple model of the mobile station transmission powers is included, enabling the estimation of the received uplink power levels at the base stations.

Round robin scheduling with prioritized re-transmissions is applied in time-domain, while no multi-user scheduling is assumed in frequency-domain. Ideal link adaptation is assumed.

When calculating the amount of transmitted bits for the scheduled users, the first step is to estimate the SINR per resource block. Next, the SINR is mapped to bits (per resource block) utilizing input from a separate link-to-system model. Each scheduled user is allocated a sufficient number of resource blocks required to empty the current transmission buffer, however not more than 50 resource blocks (10 MHz). Furthermore, a check is made for transmission errors leading to retransmissions in the coming TTIs. Finally, the correctly received bits are moved from the TX buffer to the RX buffer, and the scheduling loop is restarted again for the next TTI.

The energy saving algorithm is operating on the side of the scheduling loop described above. If the cell utilization is judged to be sufficiently low (based on some criteria, e.g. cell throughput or cell resource utilization), the cell is switched to sleep mode, meaning that all downlink transmissions are discontinued. This also means that the users are not able to access a sleeping cell. If (based on some criteria/measurements) a sleeping cell would be needed to serve close-by users, the energy saving algorithm will initiate a cell re-activation process, after which the users will be able to access the cell.



In addition to the sleep mode, fast energy saving mechanisms can be applied to active, but idle cells. For example, in case of micro sleep part of the base station, part of the base station, typically the power amplifier, is switched off in between the CRSs. In doing so, the base station power consumption can be reduced, while still keeping the cell accessible for the users.

Furthermore, in addition to the sleep mode, the underutilized cells can be switched off completely. While the power consumption will become lower compared to a sleep mode, the cell re-activation can take a longer time, which should be taken into account when designing the cell switch-off algorithm.

In case of base station power consumption models, input from previous projects, such as EARTH13, will be utilized as much as possible.



2.3.4 Dynamic cell ON/OFF power saving

This scenario studies and proposes energy efficiency strategies, applicable during low activity periods, consisting of intelligently switching on/off radio nodes. The main assumption is being in a dense urban scenario where the cell deployment is oriented to cover the peak traffic hour, being unused during certain other periods.

The scenario can consider just macro cells or heterogeneous deployments. The precision of the results obtained will be determined by the availability of reliable power models for each cell involved in this study. In case no power models can be used inside SHARING, the results will be given as % savings.

The algorithm relies on two main inputs:

1. Instantaneous traffic demand and envisaged traffic for the following period. With the former parameter, the system will be able to decide whether a given moment is suitable to apply the energy savings method, in case the amount of traffic served by the network can be handled with fewer cells. The latter one indicates if it is appropriate to launch the algorithm. It tries to anticipate the behavior of the traffic for the next minutes/hours so as to be sure it is not going to increase again above the levels on which the previously switched off cells might have to be re-switched on again.

2. Number of users allocated by the cell and users on neighboring ones. So as to be able to manage all possible interactions, these parameters must be pre-known. There are two possible approaches. A static network model, in which it is assumed that there is a network management center aware of traffic and user allocated in each cell. This management center is in charge of deciding which cells must be ON and OFF at each moment. In case of a dynamic approach, each cell knows not only its own status but also the status of the neighboring cells. If it detects a potential ON/OFF change, the cell itself is able to launch the process and force the new layout.

Figure 11 shows the process of switching off a base station in the scenario where the total amount of resultant traffic can be covered by neighbor cells (right side) and the associated process of switching on some base stations in areas where a peak in traffic is detected.

Figure 11: Dynamic cell ON/OFF power saving



The characteristics of the scenario are summarized in the following table:


Title Dynamic Cell ON/OFF power saving Task T4.3

Network topology HetNet (overlay of macro cells and small cells)


Nature of small cells LTE pico/micro/femto (possibility to include WiFi APs‡)

Environment Dense urban or urban or suburban

Context Outdoor

Inter-site distance Dynamic (ISD is a varying parameter)

Frequency deployment strategy Co-channel for LTE+LTE, separate frequencies for LTE+WiFi

Frequency bands LTE licensed bands and Wi-Fi bands

Density of small cells Dense

Backhaul Ideal backhaul between small cells

Ideal backhaul between small cells and macro cells

Ideal backhaul for all other interfaces

Propagation/channel model NA

Mobility model Static (mobility modeled as changes in traffic profiles)

Traffic model Real daily traffic patterns

Services Voice: %100 of users Data: %100 of users FTP (file size = 8 MB)


NA

KPIs, metrics involved Avg. cell throughput, %savings, µJ/bit (if power models available)


Optimize the power consumption in urban scenarios where cells may be active but unused during certain periods of the day.

Evaluation method Analytical

Links with other work packages: none.

‡ This scenario will primarily deal with LTE HetNets (LTE macro+LTE micro/pico/femto). There is a possibility to include Wi-Fi APs at a second stage. Therefore, this scenario is included in the 3rd deployment scenario which is on LTE HetNets (LTE macro+LTE micro/pico/femto).



2.3.5 HetNet mobility

This scenario is used to evaluate the mobility performance within a heterogeneous LTE deployment. Topics such as handover robustness, dual-connectivity and uplink/downlink split will be considered. The dynamical system simulation setup will in general be based on 3GPP case 1 (3GPP TR 36.814, v9.0.0).

The low power nodes can be located according to hotspot traffic or uniform random distributed locations within given binding conditions. A low power node (LPN) can create a cell by its own or be part of a combined cell scheme, where the LPN would be recognized by the UE as one of several transmission (and/or reception) points of one specific cell. For example, a Macro cell might contain several LPNs that act as distributed remote radio units (RRU) for the main macro unit, and operate with lower power that a macro main unit. Also a LPN could form its own cell with at least one carrier in the same frequency than the macro layer (co-channel deployment).

The system simulations are assumed to be dynamic where user generation, user mobility, traffic model and RRM is assumed to interact dynamically and are modeled at least on the TTI level.





Title HetNet mobility

Task T4.1




Environment Urban and suburban

Context Outdoor cells

Inter-site distance 500 meters (urban), 1732 meters (suburban) for macro

Frequency deployment strategy Same or separate frequency deployments


Density of small cells Sparse to dense, depending on number of small cells per macro cell

Backhaul Non-ideal (X2)

Propagation/channel model Path loss: As in TR 36.814 (Macro cell model 1, Pico cell model 1)

Channel model: TU14 or ITU with fast fading

Mobility model Stationary users (to create background load)

Mobile users, all moving in random direction at same fixed speed of 3, 30, 60 or 120 km/h

Traffic model FTP (download and/or upload)

Uniform or hotspot distribution

Services All users have same service

Possible addition of stationary background traffic users to increase network load


2x2 (downlink), 1x2 (uplink)

KPIs, metrics involved Average throughput for the worst 5th percentile of the users

Offered traffic , served traffic

Average throughput considering all the users

Handover failure rate


Maximizing handover robustness

Maximizing down- and uplink user throughput





2.3.6 LTE-A multi-layer network in urban/suburban environments

This scenario is based on a real environment using High Resolution (HR) map data, realistic BS/UE deployments and deterministic propagation models. All evaluated deployments are LTE co-channel scenarios, i.e. all cells in the network are operating on the same carrier frequency.

The scenario is able to address either dense urban, urban or suburban topologies. The network deployment is created over the 3D description of the real environment: The raster DTM (Digital Terrain Elevation) represents the terrain elevation with a resolution of 5 meters in the horizontal plane and a vertical precision of 1 meter, while 3D vectors represent the buildings, vegetation, bridges and water bodies with 1-meter precision in horizontal and vertical coordinates.

The macro layout is typically organized as one or more rings around a central three-sector site, macro base stations being deployed with an inter-site distance of 500 m in a hexagonal manner, as shown in Figure 12(a). The study area corresponds to the surface theoretically covered by the central macro-cells. Base stations outside this study area are nevertheless included to generate an accurate interference pattern inside the study area. The positions and heights of the sites are adjusted to consider realistic site positions, i.e. they are placed on slightly dominant rooftops, and sector antennas are distributed at the rooftop edges. Each site is composed of three sectors with directive antennas.

The outdoor small-cells (SCo) are deployed either into hotspot areas or uniformly within the whole study area with a quasi-constant Inter-Site Distance (ISD). SCo layout may be designed by an automatic deployment tool that distributes antennas along the streets with an almost-regular user-defined ISD. See the example in Figure 12(a).

Random Indoor Small-cells (SCi) deployments can be generated into building floors within the study area, based on some density user-defined parameters. An example is given in Figure 12(b). Two different SCi access modes may be selected: open or closed. In case of the closed access mode, two different types of users are simulated: subscriber or non-subscriber.

Finally, users are distributed over the simulation area in two different ways:

- Uniformly, respectively indoors and outdoors; an indoor ratio (e.g. 80%) sets the percentage of users located inside buildings.

- Or according to a traffic map that defines local user densities.

The second approach permits the non-uniform user densities (hotspots in particular) to be simulated. Therefore the ability of a given HetNet topology to manage real user traffic distributions may be assessed.

This scenario will be used to evaluate/optimize network topology & configuration to achieve targets expressed in terms of capacity, fairness, and energy at both coverage and system-level. It may also be integrated into third-party tools to confront innovative partner’s algorithms with realistic deployments and propagation models.

Figure 12: Example deployment scenarios: (a)Macro+outdoor small-cells, (b)Indoor small-cells.



The following table summarizes the characteristics of this scenario:


Title LTE-A multi-layer network in urban/suburban environments

Task T3.1, T3.2, T3.3§



Nature of small cells LTE pico/micro/femto

Environment Dense urban, urban or suburban

Context Both outdoor and indoor

Inter-site distance About 500 meters for macro-to-macro

Typically from 50 to 200 meters for small cell-to-small cell





Propagation/channel model Deterministic path-loss and channel models, based on fast and advanced ray-tracing.

Mobility model Static users in coverage-based analysis

Mobile users in system-level

Traffic model

Heterogeneous and realistic spatial distribution

Time-variant

User-profile dependent

Services Voice and data (full buffer model)


Multi-antenna systems

KPIs, metrics involved Service coverage, spectral efficiency, throughput, energy efficiency, …


Optimizing network topology & configuration to achieve targets expressed in terms of capacity, fairness, energy, etc

Evaluation method Coverage-based analysis + System-level simulations

Links with other work packages: This advanced simulation environment is used as a basis for scenarios in sections 2.3.7 and 2.4.1 which are in WP4.

§ This scenario provides a sophisticated and realistic environment for performance evaluation of HetNets. Therefore it is transversal and can be used to evaluate the performance of the proposed innovations in tasks T3.1, T3.2 and T3.3.



2.3.7 LTE-A multi-layer network in urban/suburban environments considering

non-ideal backhaul

This scenario is based on the same deployment characteristics as described in section 0. System

parameters differ from the following two aspects:

- Frequency deployment may consider Carrier Aggregation (a link with the carrier aggregation deployment scenario)

- Non-ideal backhaul, i.e. considering capacity limitations (mainly for user data transmission)



Title LTE-A multi-layer network in urban/suburban environments considering non-ideal backhaul

Task T4.1

Network topology HetNet: overlay of macro cells and small cells



Environment Dense urban, urban or suburban




Frequency deployment strategy Co-channel deployment with/without Carrier Aggregation**



Backhaul Non-ideal backhaul, i.e. considering capacity limitations (mainly for user data transmission)



Mobile users in system-level simulations

Traffic model Heterogeneous and realistic spatial distribution

Time-variant


Services Voice and data (full buffer model)





Optimizing intra-system load balancing strategies to achieve targets expressed in terms of capacity, fairness, energy, etc


Links with other work packages: This advanced simulation environment is related to the one described in the previous subsection which is in WP3.

** This scenario will investigate primarily the case without CA, with a possible extension to include CA. Therefore, it is placed in this deployment scenario on LTE HetNets (LTE macro+LTE micro/pico/femto)



2.3.8 Opportunistic ON/OFF Switching for small cells

We consider the problem of turning ON and OFF small cells as a function of the traffic load, interference levels, QoS requirements, and density of small cells. We propose a self-organizing solution in which small cells autonomously optimize their biasing, power levels and ON/OFF operations. We will also look into the coordinated case in which coupled small cells optimize their ON/OFF switching off.

The considered cost function is a function of traffic load, energy efficiency and latency. The baseline is when all small cells are turned on.



Title Opportunistic ON/OFF switching for small cells

Task T4.3

Network topology HetNet (LTE macro + LTE outdoor pico/micro)


Nature of small cells LTE pico/micro

Environment Urban

Context Both outdoor and indoor areas

Inter-site distance 500 m for macro




Backhaul X2


A mixture of many models:

3GPP Path loss model for macro and small cells as specified in TR 25.81415, TR 36.81416 + NLOS building penetration (constant for outer wall + distance-dependent indoor loss + additional loss for heavier indoor walls)

Lognormal shadowing with exponential spatial correlation for all links (std and correlation distance will vary).






1x1 (downlink)

KPIs, metrics involved

Average throughput per user (-> worst 5th percentile of users -> coverage)

Offered area traffic or served area traffic (Mbps/km2, GB/h/km2 -> capacity)

Total network power consumption (per hour)

kW/km2

Daily energy consumption (accumulated over 24 hours)


Minimizing total energy consumed by macro cells and small cells through intelligent sleep mode and/or cell switch ON/OFF schemes

Evaluation method Analytical and system level simulations (MATLAB)




2.3.9 Backhaul offloading via opportunistic caching

This scenario looks into the problem of backhaul offloading via proactive small cell BSs. It is assumed that the bottleneck lies at the backhaul, whereby users request a number of video files for a library. The classical case posits that all contents lie at the core network and hence the backhaul suffers from congestion when the network gets overloaded. Instead, we leverage caching and storage capabilities at the edge in which small cells pre-cache judicious contents during off-peak backhaul demands. By pushing the contents at the network edge a higher user satisfaction is obtained and the backhaul is offloaded. We will consider both spatial and social caching exploiting both storage and social networking.

Backhaul is currently one of the most important aspects of network design and will continue to gain importance in light of the fact that 70% of all contents will be video by 2017.



Title Backhaul offloading via opportunistic caching

Task T4.4

Network topology HetNet (LTE macro + LTE outdoor pico/micro)



Environment Urban






Backhaul X2



3GPP Path loss model for macro and small cells as specified in TR 25.81417, TR 36.81418.

+ NLOS building penetration (constant for outer wall + distance-dependent indoor loss + additional loss for heavier indoor walls)




Traffic model Non full buffer



1x1 (downlink)




Average backhaul load saving


Proactive caching as a means of decreasing the load and backhaul offload

Evaluation method Analytical using simulation based and possibly real traces.


Celtic-Plus SHARING

Public distribution

2.3.10 Power saving through sum powe

In this scenario we will evaluate the performance of decentralized interior point log barrier type algorithm19 for heterogeneous deployment

The proposed power optimization minimizes weighted sum of the base station transmit powers with the requirement of minimum QoS for the cell edge UEs. The optimization problem is solved by the means of interior point methods, more precisely by the means of log barrier interior point optimization. In a second step, we propose a decentralized version of the log barrier interior point optimization by the means of consensus averaging20.

The power saving is considered in downlink s

The shopping mall HetNet deployment is shown in

Figure

Power saving through sum power optimization in HetNet deploy

In this scenario we will evaluate the performance of decentralized interior point log barrier type deployment of small cells in a shopping mall.

The proposed power optimization minimizes weighted sum of the base station transmit powers with the requirement of minimum QoS for the cell edge UEs. The optimization problem is solved by the means of

more precisely by the means of log barrier interior point optimization. In a second step, we propose a decentralized version of the log barrier interior point optimization by the

The power saving is considered in downlink scenario, then an extension will be proposed for uplink

ment is shown in the figure below:

Figure 13: Shopping mall HetNet deployment

Document D.2.2

43 (115)

r optimization in HetNet deployments

In this scenario we will evaluate the performance of decentralized interior point log barrier type

The proposed power optimization minimizes weighted sum of the base station transmit powers with the requirement of minimum QoS for the cell edge UEs. The optimization problem is solved by the means of

more precisely by the means of log barrier interior point optimization. In a second step, we propose a decentralized version of the log barrier interior point optimization by the

cenario, then an extension will be proposed for uplink.



The details of the scenario are given in the following table:


Title Power saving through sum power optimization in HetNet deployment

Task T4.3



Nature of small cells LTE-A Pico

Environment Dense urban

Context Outdoor

Inter-site distance

500 meters for macro-to-macro

Pico cell shopping mall of dimension 400x400 m

19 tri-sector macros with wrap around



Density of small cells Dense in the shopping mall and with random deployment

Backhaul

Ideal backhaul between small cells

Ideal backhaul between small and macro cells

And possibly non-ideal backhaul between small and macro cells

Propagation/channel model 3GPP case 1 for macro and assumptions of 3GPP TR 36.81421 for small cells

Mobility model Users static within small cells/macro base stations

Traffic model Regular Poisson distribution based arrivals with higher intensity in Hotspot and regular intensity in all other areas

KPIs, metrics involved Cell Edge Throughput, Cell Edge SINR, Cell Center Throughput, Cell Center SINR


Optimization of the weighted sum of transmit powers based on interior point techniques and their decentralization with cell edge SINR constraint (above -6dB) and cell edge throughput constraints.


Links with other work packages:

The scenario is used in T4.3 as baseline for the evaluation of the ON/OFF power saving and distributed power saving in more complicated macro interference dependent scenario. The scenario have link with WP6 since it assume new coordination node ( campus GW ) that coordinate between radio resources management (RRM) actions in the campus additional signaling and/or interfaces are needed for such common RRM.



2.3.11 Antenna Smart Grid Solutions for Outdoor DAS (Distributed Antenna

Systems)

In this scenario the aim is to study Outdoor DAS solutions as part of HetNet evolution. Performance is compared to macro cell and small cells solutions, where macro DAS scenarios are studied. Capacity

oriented system level simulations are used to evaluate scenarios benefits.



Title Antenna Smart Grid Solutions For Outdoor DAS

Task T.6.2

Network topology

Macro cell/

Small cell and

HetNet (overlay of macro cells and small cells)

Radio Access Technology (-ies) LTE-A or HSPA††

Nature of small cells LTE pico/micro/femto OR HSPA pico/micro/femto

Environment Business district

Context Outdoor

Inter-site distance To be determined

Frequency deployment strategy Part of evaluations

Frequency bands LTE/HSPA licensed bands

Density of small cells Dense and sparse

Backhaul NA


Path loss: Okumura-Hata (parameters)

Shadowing: lognormal with exponential spatial correlation (parameters)


Possibly also Ray Tracing

Mobility model NA

Traffic model NA

Services NA


NA

KPIs, metrics involved Capacity and topology comparison


Small Cells versus Outdoor DAS

Hybrid Solutions

Layer Methodologies



†† This scenario will basically study LTE-A, with a possible extension to HSPA. Therefore, it is included in this deployment scenario.



2.3.12 Joint interference and location prediction

At the physical layer, local radio propagation conditions (including interference levels) obviously depend on the relative positions of mobile users and fixed elements of infrastructure. Instead of instantaneous channel state information, some inter-cell interference cancellation and/or coordination (ICIC) schemes rely on averaged-type information such as location and/or average power levels of signal/interference. As an example, ICIC mechanisms may rely on 2D fingerprinting maps (constructed online or offline), based on measurements performed at the mobile terminals, as investigated in the 3GPP LTE mobile femto/macro cellular context.

The objective of the research carried out in this scenario is to exploit radio metrics at the PHY layer that are available at the users via the downlink communications from base stations and via communication between users. These measurements are used to obtain joint location/tracking of mobile terminals and iterative prediction of local propagation conditions and interference. The expected advancements on this topic are two-fold. First we aim at defining semi-deterministic interference models that take into account uncertainty of estimated positions (mobile users and fixed elements) as well as theoretical links between spatial correlation of radio metrics and mobility profile of users (e.g. maximum mobile speed, orientation vector) in order to improve interference prediction. Secondly, we aim at designing adaptive cooperative positioning and tracking algorithms in order to assist subsequent location-based ICIC mechanisms and enable better in-site DSA (Dynamic Spectrum Access), for instance considering cooperative Kalman filter that includes adaptive state models (positioning and speed as state vector and hidden state variable being the propagation model).



Title Joint location and interference prediction for ICIC

Task T3.3



Nature of small cells LTE-A pico

Environment Dense urban, Urban or rural


Inter-site distance 500 or 1732 meters for macro-to-macro

250 meters for macro to pico

100 meters for pico to pico



Density of small cells Depends on the configuration

Backhaul Ideal backhaul for all interfaces


3GPP Path loss model for macro and small cells22.

Shadowing: lognormal with exponential spatial correlation23

Fast fading: Typical Urban (TU) model24

Mobility model Depends on the configuration (from 3km/h to 90 km/h)

Traffic model Full buffer

Services 100% of users sending measurement reports to the eNB.


To be defined (from 1x1 to 4x2)

KPIs, metrics involved - Cdf of the positioning error over the area of interest - Prediction accuracy of interference level versus confidence in a

particular location


Determine interference condition with respect to relative positioning of mobile nodes through semi deterministic definition of interference model taking into account positioning uncertainty.

Evaluation method - Semi analytical

- System level simulations




2.3.13 Performance and energy efficiency evaluation in LTE Heterogeneous

Networks

Energy efficiency is becoming an important metric to assess the performance of advanced communication networks, along with capacity and network QoS. This scenario aims to jointly evaluate energy consumption and user throughput using system level simulations and under realistic traffic models and realistic load evolution. Guidelines for optimizing the energy efficiency in heterogeneous networks (for deployments including macro cells and small cells) are expected as an outcome of this study. Investigations will consider whether increasing the number at the macro cells can reduce the number of small cells required to cope with the load evolution.



Title Performance and energy efficiency evaluation in LTE Heterogeneous Networks

Task T4.1





Context Outdoor

Inter-site distance macro-to-macro: 500m


Frequency bands LTE licensed bands 2GHz

Density of small cells Sparse (2 to 4 per macro cell)

Backhaul Non-ideal backhaul for all interfaces

Propagation/channel model 3D indoor channel model

Mobility model 3 km/hr

Traffic model FTP model 1 as defined in TR 36.814

Services Data (file size = 8 MB)


2x2

KPIs, metrics involved Average cell throughput and cell-edge (5% percentile)


Maximizing capacity of a hetnet while minimizing power consumption





2.4 Deployment scenario 4: Inter-RAT HetNet (LTE + WiFi)

Deployment scenario 4 gathers the following 4 scenarios involving Inter-RAT HetNet (LTE + Wi-Fi):

1. Multi-RAT Heterogeneous networks in urban/suburban environments 2. LTE-WiFi Offloading 3. Seamless Offloading in Heterogeneous Wireless Networks 4. Positioning in Heterogeneous Converged networks

2.4.1 Multi-RAT Heterogeneous networks in urban/suburban environments

This scenario is based on the same deployment characteristics as described in section 2.3.6. System

parameters differ from the following two aspects:

- LTE-A cells + WiFi APs

- Wi-Fi unlicensed bands



Title Multi-RAT Heterogeneous networks in urban/suburban environments

Task T4.2, T4.3, T4.4‡‡

Network topology HetNet: overlay of macro cells and small cells

Radio Access Technology (-ies) LTE-A and WLAN

Nature of small cells LTE pico/micro/femto and Wi-Fi APs

Environment Dense urban, Urban or Suburban




Frequency deployment strategy LTE bands and WiFi bands

Frequency bands LTE licensed bands and Wi-Fi unlicensed bands





Mobile users in system-level

Traffic model

Heterogeneous and realistic spatial distribution

Time-variant


Services Voice and data (full buffer)





Optimizing inter-system load balancing strategies to achieve targets expressed in terms of capacity, fairness, energy, etc


Links with other work packages: This advanced simulation environment has common deployment characteristics with the one described in the subsection 2.3.6 which is in WP3.

‡‡ This scenario provides a sophisticated and realistic environment for performance evaluation of multi-RAT HetNets (LTE+Wi-Fi). Therefore it is transversal and can be used to evaluate the performance of the proposed innovations in tasks T4.1, T4.2 and T4.3.



2.4.2 LTE-WiFi Offloading

This scenario looks into the problem of dual mode small cells transmitting simultaneously on LTE and WiFi bands. The problem boils down to designing self-organizing load balancing solutions that optimally balances the load among both RATs. The proposed approach is based on reinforcement learning techniques adapted to the specifics of the problem. In addition, the objective function is the aggregate spectral efficiency and cell edge performance.



Title LTE-WiFi offloading

Task T4.2

Network topology HetNet (macro + outdoor pico)

Radio Access Technology (-ies) LTE(-A) and WiFi

Nature of small cells LTE pico/micro/WiFi

Environment Urban



Frequency deployment strategy LTE licensed and WiFi unlicensed bands

Frequency bands LTE licensed and WiFi unlicensed bands


Backhaul X2



3GPP Path loss model for macro and small cells as specified in TR 25.81425, TR 36.81426







1x1 (downlink)





How to efficiently integrate small cells and WiFi? How to leverage WiFi to improve LTE transmissions





2.4.3 Seamless Offloading in Heterogeneous Wireless Networks

This scenario presents a seamless offload scheme between 3GPP and WLAN§§ networks for real-time packet traffic. This solution would carry heterogeneous mobile broadband networks' fundamental identifiers such as application targets, user's mobility and usage characteristics, network capacity and density, network management, terminal's power state, network congestion status, physical channel parameters to a mathematical plane.

With this model, by taking into account the base protocol functions such as access, error and congestion control, network and channel capacity, data transmission latency and reliability parameters will be improved based on the upper and lower limits of them.

The scenario will allow us to contribute to the following points: 1- User preference as a QoE metric during handover decision will be integrated into the developed MADM (multiple attribute decision making) algorithm. 2-Handover execution will be handled not only based on link-quality including QoS values but also based on subjective measures. 3- Network traffic of the Heterogeneous Wireless Networks (HWNs) will be improved and managed based on user experience.

The scenario is depicted in Figure 14, and consists of UTRAN/GERAN access networks along with a WiFi access point in tight-coupling heterogeneous network architecture.

Figure 14: IEEE 802.21 and user experience based offload scenario

§§ In the deliverable, the terms WLAN, WiFi and Wi-Fi are used in a synonymous manner.





Title Data Offloading in Heterogeneous Wireless Networks

Task T4.2


Radio Access Technology (-ies) HSPA (or UMTS) and WLAN***

Nature of small cells Wi-Fi APs

Environment Urban

Context Indoor

Inter-site distance

500 meters for macro-to-macro

20 meters for small cell-to-small cell

100 meters for macro-to-small cell

Frequency deployment strategy Separate frequency deployment

Frequency bands HSPA licensed bands and

Wi-Fi unlicensed bands (2.4G)


Backhaul





Path Loss models:

802.11: Two-ray Ground Reflection

3G: Path loss UE between BS: L(d) = L_init + 10 * n * log10(d) 27

Here d is the distance between the Node B and the UE in km, L_init is the distance loss at 1 km distance and n is the decay index. L_init and n depends on the environment.

Mobility model The percentage of cell-edge users is 10%. Random Waypoint mobility models is used

Traffic model Background traffic : Poisson arrivals

Services

The availability of flat-rate voice and data bundles, and higher demand for entertainment services like YouTube, Apple’s iTunes and services such as video streaming from Television Networks.


1x1


Client SNR (dB)

PSNR (dB)

Data received (Kbps):

Packet Loss (%):

Throughput (Kbps):

MOS Value (Mean Opinion Score)


The main purpose of this scenario is to find out a smart way to switch between 3GPP data networks and WiFi networks. For this report, we will be evaluating a client based offloading implementation and a QoE (Quality of Experience)-based innovative data offloading strategy

Evaluation method Analytical and System level simulations


*** Although this scenario involves HSPA, it is included in this deployment scenario (but not in the HSPA deployment scenario) since its main focus is offloading cellular traffic to Wi-Fi.

Celtic-Plus SHARING

Public distribution

2.4.4 Positioning in Heterogeneous Converged Networks

This scenario is used to study the benefits and the performance of hybrid localization architecture in heterogeneous converged networks. The localization architecture is based on extending EMinimization of Drive Tests functionality to incorporate the RF fingerprints of LTE and WiFi small cells.

The scenario consist of a set of overlaying macro base stationsLTE or WiFi small cells (red circles) operating on different frequency band as depicted in users are uniformly distributed and they move between the cells with constant velocMDT measurement. By processing the MDT measurement reports, operator can construct a database and estimate the location of UEs or the location of randomly deployed small cells. The studied performance indicators consist of positioning er

Figure 15: Simulation scenario of converged heterogeneous small cell network

Analyzing the performance of positioning accuracy in this scenario allows us to draw conclusions of the expected performance of various location assisted RRM algorithms such as network based proximity indication which is used for example to trigger UE measurements accuracy of the proximity indication affects for example the and missed offloading opportunities. Hence, impacting on the UE energy efficiency, small cell utilization and offloading gains. The research is carried out by using a dynamic system level simulator modeling accurately downlink transmissions and particularly the received signal power and quality at UE‘s antenna connector.

Document D.2.2

Positioning in Heterogeneous Converged Networks

This scenario is used to study the benefits and the performance of hybrid localization architecture in eneous converged networks. The localization architecture is based on extending E

functionality to incorporate the RF fingerprints of LTE and WiFi small cells.

The scenario consist of a set of overlaying macro base stations (black arrows) and randomly distributed LTE or WiFi small cells (red circles) operating on different frequency band as depicted in users are uniformly distributed and they move between the cells with constant velocity doing periodic MDT measurement. By processing the MDT measurement reports, operator can construct a database and estimate the location of UEs or the location of randomly deployed small cells. The studied performance indicators consist of positioning error (PE) and confidence of the location.

Simulation scenario of converged heterogeneous small cell network

Analyzing the performance of positioning accuracy in this scenario allows us to draw conclusions of the d performance of various location assisted RRM algorithms such as network based proximity

indication which is used for example to trigger UE measurements preceding offloading to small cells. The accuracy of the proximity indication affects for example the amount of unnecessary network monitoring and missed offloading opportunities. Hence, impacting on the UE energy efficiency, small cell utilization

The research is carried out by using a dynamic system level simulator modeling ly downlink transmissions and particularly the received signal power and quality at UE‘s antenna

Document D.2.2

page 52 of 115

This scenario is used to study the benefits and the performance of hybrid localization architecture in eneous converged networks. The localization architecture is based on extending E-UTRAN

functionality to incorporate the RF fingerprints of LTE and WiFi small cells.

(black arrows) and randomly distributed LTE or WiFi small cells (red circles) operating on different frequency band as depicted in Figure 15. The

ity doing periodic MDT measurement. By processing the MDT measurement reports, operator can construct a database and estimate the location of UEs or the location of randomly deployed small cells. The studied

Simulation scenario of converged heterogeneous small cell network

Analyzing the performance of positioning accuracy in this scenario allows us to draw conclusions of the d performance of various location assisted RRM algorithms such as network based proximity

offloading to small cells. The amount of unnecessary network monitoring

and missed offloading opportunities. Hence, impacting on the UE energy efficiency, small cell utilization The research is carried out by using a dynamic system level simulator modeling

ly downlink transmissions and particularly the received signal power and quality at UE‘s antenna





Title Positioning in HetNet (LTE only and LTE+WiFi)

Task T6.2

Network topology Inter-frequency HetNet (overlay of macro cells and small cells operating on different carriers)

Radio Access Technology (-ies) LTE only or LTE + WLAN

Nature of small cells LTE femto OR Wi-Fi

Environment Dense urban, Urban or Rural

Context Outdoor OR Outdoor + Indoor

Inter-site distance

From 500 to 1750 meters for macro-to-macro

Random, minimum 50 meters for small cell-to-small cell

Random, minimum 75 meters for macro-to-small cell

Frequency deployment strategy Separate frequency deployment for macro and small cells.

Frequency bands LTE licensed band AND Wi-Fi unlicensed bands (2.4G)

Density of small cells 3 small cells per macro cell (sparse).

Backhaul

Ideal backhaul between small cells.

Idea backhaul between small cells and macro cells.

Idea backhaul for all other interfaces.



Shadowing: lognormal with exponential spatial correlation as specified in TS 36.83930.

Fast fading: Typical Urban (TU) model as specified in TS 36.83931.

Mobility model 100% of users at 3 km/hr or 30 km/h

Traffic model 100% Background loading

Services 100% of users sending measurement reports to the eNB.


Receiver: 1x2 MRC


Primary KPIs

- Cumulative distributions of positioning error over the area of interest.

- 68%-ile and 95%-ile prediction accuracies over the area of interest.

- Prediction accuracy vs. confidence in a particular location


Development of hybrid localization architecture relying on 3GPP specifications for network based position estimation of UEs and uncoordinatedly deployed small cells e.g., WiFi access points with unknown location. The proposed solution is based on Radio Frequency (RF) fingerprint localization of enhanced MDT architecture using radio measurements obtained from 3GPP and Wi-Fi networks. The goal is to study:

- How accurately location of an unknown UE or small cell can be acquired by means of RF fingerprinting;

- Can the estimated location be used to trigger the measurements on the small cell layer when UE is in the proximity of the small cell

Evaluation method System level simulations and performance measurements in LTE/WLAN environment.

Links with other work packages: This scenario is related to the one in section 2.3.12 (WP3) which also

deals with location prediction.



2.5 Deployment scenario 5: Relays

Deployment scenario 5 gathers the following 4 scenarios involving relays:

1. LTE-Advanced collaborative relaying 2. Clustered wireless mesh networks based on LTE 3. Joint channel and Network coding for advanced networks

4. Path loss model for relay

2.5.1 LTE-Advanced collaborative relaying

We consider two collaborative relaying scenarios as depicted in Figure 16 andErreur ! Source du renvoi introuvable.. The first corresponds to the case where two RN can collaborate behind the same eNB to cover UEs which are shadowed from the DeNB (Donor eNB). The key techniques to exploit here are the use of distributed spatial-multiplexing and collaborative-scheduling at the RNs (Relay Nodes). Adaptations to coded-modulation and HARQ protocols to allow for the two techniques to be achieved will be proposed primarily for TDD-based relays. Both decode-and-forward as well as more sophisticated quantize-map-and-forward techniques will be considered. Two-way coding strategies may also be studied. The second scenario is a so-called relay-aided broadcast channel and aims to use a RN in scenarios where multiple UEs are covered by both the DeNB and the RN. Here the RN provides additional spatial-streams in a causal fashion and aims to increase the level of spatial-multiplexing offered by the DeNB. Again, adaptations to coded-modulation and HARQ protocols to allow for such improvements will be proposed.

Figure 16: Multi-Relay Collaboration Scenario



Figure 17: Relay-aided Broadcast



Title LTE-Advanced collaborative relaying

Task T5.1

Network topology

Cellular network comprised of:

• Donor eNB (main eNB of cell) =>scheduling for UE and RN, common control channels, radio resource control master, first gateway to Core Network

• Relay nodes (RN) =>scheduling, common control channels, radio resource control slave to D-eNB, master for UE

• User equipment (UE), end nodes => can connect to either RN or D-eNB, should be the same for UE, slaves to RN or D-eNB

Radio Access Technology (-ies) LTE, LTE-A

Nature of small cells Relay nodes

Environment Urban and sub-urban coverage extension

Context Outdoor and Outdoor-to-Indoor

Inter-site distance NA


Frequency bands (LTE) licensed bands

Density of small cells Dense enough to enable collaboration

Backhaul Backhaul to core network only available at the donor eNB


Path loss: Okumura-Hata

Shadowing: lognormal with exponential spatial correlation (parameters)

Fast fading: Rayleigh & Rice

Mobility model Semi-static (fixed once deployed)

Traffic model NA

Services NA


Up to 2 antennas per donor eNB and relay node

KPIs, metrics involved Achievable throughput on relayed eNB-UE link, end-to-end throughput (core network to user)


How to improve the throughput on the relayed eNB-UE link using cooperation between two or more relays using

• Space-time coding • AMC and HARQ protocols

Evaluation method

Link level simulations,

System level simulations,

Experiments with OpenAirInterface




2.5.2 Clustered wireless mesh networks based on LTE

The aim of this scenario is to study the performance of wireless mesh network (or network extensions) based on LTE. 3GPP is working actively on Private Mobile Radio (PMR) systems, in order to provide LTE solutions to this market. However PMR systems have very often completely different requirements and use cases, with respect to traditional cellular communications. Much more emphasis is given, for instance, to security, reliability and robustness of the communication, rather than throughput.

The scenario focuses on rapidly deployable wireless mesh networks, to be used to cover an area which is not covered by fixed PMR infrastructure. It could be the case after a major natural disaster. Another possible use case is to deploy a wireless network in areas without any infrastructure, or where the main governmental or market actors do not want to invest on a wired infrastructure. A third use case, similar to coverage extension with relays, is when there is a specific and limited area which is not covered by the fixed network, then a wireless extension can be imagined with possibly multiple relays. Here we will focus on the first use case.

In order to reuse as much as possible the LTE structure and protocols, we propose a clustered wireless mesh network meaning that the network is divided in clusters, mapping (or equivalent) to LTE macro (or micro, depending on the available transmit power, type of equipment, etc.) cells. Each cell is controlled by a ClusterHead (CH) which is acting as a 3GPP eNodeB. Mesh Routers (MR) are acting as 3GPP relays, and inherit relay and UE procedures from LTE. Some MRs, connected to 2 or 3 clusters, are called bridging MRs and allows communications between the clusters. Common bridging MRs between the clusters can cooperate for improving performance (robustness, throughput, latency). Edge MRs are possibly present, if the wireless mesh network is not independent but it is linked to other networks via other technologies. A schematic representation of the clustered mesh network is given in the figure below.

Cluster-Head (CH)

Mesh Router (MR)

Bridging Mesh Router (BMR)

Edge Mesh Router (EMR)

Other Access Technology

Exploitable links (example)

Figure 18: Clusterized wireless mesh network

In this scenario, one critical aspect is inter-cluster communications. To the best of our knowledge, 3GPP LTE Rel 10 does not authorize a Relay Node (RN) to attach to more than one Donor eNodeB, even though many Donor eNodeB may be available during the initialization phase. This scenario will help testing, at PHY layer, cooperative relaying strategies that enable efficient and robust inter-cluster communications. Another challenging aspect of this scenario is the design of adequate radio resource strategies and their corresponding signaling mechanisms that are able to efficiently exploit the benefit brought by the use of cooperative relaying techniques in this networking setting.





Title Clustered wireless mesh networks based on LTE

Task T5.1

Network topology

Mesh network comprised of:

• Clusterheads (CH) – act as 3GPP eNodeB => scheduling, common control channels, radio resource control master

• Mesh routers (MR) – act as 3GPP relay nodes • Bridging–MR (Relays) which are connected to 2 or 3 CH

and forward traffic between clusters => collaborative forwarding and distributed signal processing are possible


Nature of small cells NA, here relays (fixed/nomadic) are used for communications between clusters (i.e. cells)

Environment Urban, Suburban, Rural

Context Outdoor

Inter-site distance

Few kilometers, for macro-to-macro

NA for small cell-to-small cell

NA for macro-to-small cell

Frequency deployment strategy Co-channel deployment (Inband) or

Separate frequency deployment (out of band or frequency division of a common band)

Frequency bands LTE licensed bands (in particular 700 MHz band, i.e. B14 which is used also for PMR in the USA)

Possibly 400 MHz band used for PMR is Europe


Backhaul NA


Path loss: Okumura-Hata or some other model adapted to the 700 MHz and 400 MHz band, possibly abstracted in CQI

Shadowing: lognormal with iid variables, or absent

Fast fading: Rayleigh, 3GPP channel models

Mobility model 100% users at the same speed, from pedestrian to vehicular values

Traffic model Generic traffic : with Poisson process for simulations

Full buffer constant traffic for PHY simulations

Services Voice: 100% of users

Data: TBD% of users FTP, TBD% of users video streaming


eNodeB (CH) and MR: maximum 2x2

UE: maximum 1x2


For simulation:

• Packet Error Rate • Avg. cell/user throughput • Avg. user delay • Avg. fairness index • Min throughput • Max delay • Max drop rate

For demonstration†††

• Achievable throughput/delay/BLER on relayed CH-CH link

• end-to-end throughput/delay/BLER (core network to user)

††† This scenario is a common one between TCS and EUR. The theoretical studies will be done in WP5 (TCS) and a prototyping/demonstration is foreseen in WP7 (EUR).




Maximizing sum rate, minimizing delay and maximizing fairness among users in mesh deployment.

Study cooperative relaying strategies for inter-cluster communications in a wireless mesh network, with focus on robustness rather than throughput, for PMR services.

Evaluation method


MAC level simulations

Experiments with OpenAirInterface‡‡‡

Links with other work packages: This scenario is a common one between TCS and EUR. The theoretical studies will be done in WP5 (TCS) and a prototyping/demonstration is foreseen in WP7 (EUR).

‡‡‡ This scenario is a common one between TCS and EUR. The theoretical studies will be done in WP5 (TCS) and a prototyping/demonstration is foreseen in WP7 (EUR).

Celtic-Plus SHARING

Public distribution

2.5.3 Joint channel and n

The Multi-Access Relay-channel (MARC) model presented in Equipment (UEs) using a relay node (RN), to communicate with a small cell (SC).

Figure 19

The amount of data transmitted by the relay is usualby the sources. Thus, in order to reduce the overload of the relay backhaul link, network coding (NC) can be advantageously used at the relays: instead of separately relaying data packets for each of theusers accessing the channel, the NC technique allows combining them together for transmission. Network coding has many advantages such as maximizing throughput and minimizing energy per bit.

In our work we will consider the case of using multiple relays the intermediate nodes and to have a larger coverage extension model has received much attention, studies about NC on multistill scarce due to its complexity. In particular, we will consider the scheme with 2 sources, 2 relays and 1 destination as depicted in Figure 20RN2) to communicate with a final destination: in this case it only concerns uplink transmissions. UEs’

transmissions are encoded by two channel codes

used at the relay i=1, 2. The destination decodes the received signals using knowledge of

and 2R

CCCC . Consequently, a joint-design of network (

order to fully exploit these two coding techniques.

Our approach is based on using binary Lownon-binary (NB) network coding, which offers a natural generalization of binary network coding and allows further optimization of the design.

Joint channel and network coding for advanced networks

channel (MARC) model presented in Figure 19 shows two (or more) User Equipment (UEs) using a relay node (RN), to communicate with a small cell (SC).

19: Multi-access relay-channel model

The amount of data transmitted by the relay is usually equal to the amount of data initially transmitted by the sources. Thus, in order to reduce the overload of the relay backhaul link, network coding (NC) can be advantageously used at the relays: instead of separately relaying data packets for each of theusers accessing the channel, the NC technique allows combining them together for transmission. Network coding has many advantages such as maximizing throughput and minimizing energy per bit.

In our work we will consider the case of using multiple relays in order both to alleviate the data load at the intermediate nodes and to have a larger coverage extension average. But, though NC for the MARC

received much attention, studies about NC on multi-user multi-relay wireless networks are due to its complexity. In particular, we will consider the scheme with 2 sources, 2 relays and

20. It illustrates the case when 2 UEs use two relay nodes (RNicate with a final destination: in this case it only concerns uplink transmissions. UEs’

transmissions are encoded by two channel codes 1CCCC and

2CCCC

respectively, while a network code

=1, 2. The destination decodes the received signals using knowledge of

design of network (1R

CCCC ,2R

CCCC ) and channel (1CCCC ,

2CCCC ) codes is desired in

order to fully exploit these two coding techniques.

Our approach is based on using binary Low-Density Parity-Check (LDPC) codes as channel codes and binary (NB) network coding, which offers a natural generalization of binary network coding and

allows further optimization of the design.

Document D.2.2

59 (115)

shows two (or more) User

ly equal to the amount of data initially transmitted by the sources. Thus, in order to reduce the overload of the relay backhaul link, network coding (NC) can be advantageously used at the relays: instead of separately relaying data packets for each of the users accessing the channel, the NC technique allows combining them together for transmission. Network coding has many advantages such as maximizing throughput and minimizing energy per bit.

in order both to alleviate the data load at verage. But, though NC for the MARC

relay wireless networks are due to its complexity. In particular, we will consider the scheme with 2 sources, 2 relays and

. It illustrates the case when 2 UEs use two relay nodes (RN1 and icate with a final destination: in this case it only concerns uplink transmissions. UEs’

respectively, while a network code RiCCCC is

=1, 2. The destination decodes the received signals using knowledge of 1CCCC ,

2CCCC ,

1RCCCC

) codes is desired in

C) codes as channel codes and binary (NB) network coding, which offers a natural generalization of binary network coding and

Celtic-Plus SHARING

Public distribution

Figure 20: Two-users and t


Scenario aspect

Title

Task

Network topology

Radio Access Technology (-ies)

Nature of small cells

Environment

Context

Frequency deployment strategy

Frequency bands

Density of small cells

Backhaul


Mobility model

Traffic model

Services




Evaluation method


Document D.2.2

users and two-relays communication system


Description

Joint channel and Network coding for advanced networks

T5.1

Two users connecting to a base station directly and via two relays

LTE –like with LDPC codes

NA

Urban or Suburban or rural

Outdoor

NA

NA

NA

Non-ideal backhaul between relay and Donor eNB

AWGN, block fading

NA

NA

NA

1

Bit Error Rate

Analysis and design of Low-Density Parity-Check (LDPC) codes for Joint Network-Channel Coding (JNCC) over the Multiple Access Relay Channel model with two users.


Document D.2.2

page 60 of 115

Joint channel and Network coding for advanced networks

Two users connecting to a base station directly and via two

n relay and Donor eNB

Check (LDPC) codes Channel Coding (JNCC) over the Multiple



2.5.4 Path loss model for relay scenario

This scenario considers a network densification with relays to increase the average or cell edge user throughput. The benefits of the relay solutions are generally evaluated by system level simulations which define many simulation parameters such as path loss models. From a propagation point a view, the multiple links between a relay, a mobile and a base station can be modeled by two propagation channel models (Figure 21). The first one is the model for a macrocell environment that applies for the macro link between a base station and a mobile or for the backhaul link between a base station and a relay. The difference between a relay or a mobile is given by the antenna height. The second one is the model for a microcell environment that applies for the access link between a relay and a mobile. Path loss models may significantly change simulation results. That’s why path loss models need to be realistic and well-balanced. Models that underestimate the path loss for the macro link and overestimate the path loss for the access link will not promote relay-based solution or microcell densification.

Figure 21: Propagation channel environments for relay scenario

As a first step, a measurement campaign will be performed in an outdoor urban environment. Path loss models for the macro, backhaul and access links will be calibrated with the collected data. The results will be compared with well-known propagation channel models such as COST231-HATA, Winner, 3GPP or ITU channel models. The data analysis will focus on the impact of the relay position (X-Y location and antenna height) on the path loss models.

Next, the ability of the propagation channel to relay the signal will be analyzed. Previous system-level simulations consider traffic assumptions and/or take into account interferences to analyse the throughput but they do not analyze accurately the impact of the propagation channel. A single user scenario will be defined and the theoretical throughput with and without a relay will be calculated using the Shannon capacity equation. The potential gain provided by the relay will only depend on the path loss ratio between the base station, relay and mobile. Different path loss models or measurement data will be used for this study.

hMS

BS

MS

hRS

RS

MS

Microcell environment

RS

Macrocell environment





Title Propagation channel path loss

Task T5.1

Network topology Relay network

Radio Access Technology (-ies) Agnostic§§§

Nature of small cells Microcell and macrocell with relay

Environment Urban

Context Outdoor

Inter-site distance Distance BS-MS max: 1000 m

Frequency bands 2 GHz

Propagation/channel model Path loss for macrocell and microcell environment will be defined from measurement data


SISO

KPIs, metrics involved Path loss and theoretical throughput


1. Path loss and shadowing characterization for the backhaul and access links

2. Measurement based throughput estimation with relay

Evaluation method Measurement campaign and Shannon capacity

Links with other work packages: Recommendations for propagation channel models will be provided to assist WP3, WP4 and WP5 participants in using realistic and consistent multi-link path loss models in urban outdoor environment.

§§§ Although the RAT for this scenario is transparent, the 2 GHz frequency band hints at IMT technologies (GSM900, GSM1800, UMTS, HSPA, LTE/LTE-A).



2.6 Deployment scenario 6: Device-to-Device (D2D)

Deployment scenario 6 gathers the following 3 scenarios involving Device-to-Device communications:

• D2D Unicast communications • D2D broadcast communications • D2D communications for overloaded networks

The standardization work on D2D technologies in 3GPP is focused on a set of scenarios, which were identified to fit the needs of both public safety and commercial mobile networks. D2D technologies make use of the direct LTE radio interface between the devices instead of going through the network infrastructure. The main scenarios supported by D2D technologies are:

• Direct 1:1 (one-to-one) communications, also named as D2D unicast communications • Direct 1:many (one-to-many) communications, also named as D2D broadcast communications

As shown in the table below, the direct 1:1 communication scenario is designed to support the usual data communication service between two users. The direct one-to-many communication scenario is designed in order to support a new groupcast service called as “Group Communication”. One UE broadcasts messages to other UEs that can be in proximity within the same cell (area controlled by a single eNodeB) or in different cells (areas possibly controlled by multiple eNodeBs). These scenarios should be under the control of the network operator and can happen in various situations, either through the cell coverage (Figure 22) or out of cell coverage (Figure 23).

Figure 22: LTE D2D scenarios Within Network Coverage

In the case of out of cell coverage situation, UEs may communicate directly with each other via LTE technology, and the communication may happen with or without network assistance (e.g. signaling and control).

Situations D2D Unicast

Communication

D2D broadcast

Communication

In Cell coverage x x

Out of cell coverage x x

Coverage extension with UE relay

x x



Figure 23: LTE D2D scenarios Outside Network Coverage

Furthermore, 3GPP defines a UE relaying feature which is used for public safety scenarios. This feature is applicable in situations such as UE-to-Network relaying (when a UE is relayed to the network by a UE with relaying capabilities called UE-Relay) or UE-to-UE relaying (when a UE is relayed to another UE with the help of a UE Relay).



2.6.1 D2D Unicast communications

This scenario will address two issues:

- efficient radio resource allocation schemes, and - optimal control channel design

for 2D2 Unicast communications fitting all situations (inter-cell communication, in and without cell coverage, extension with UE relay).



Title D2D Unicast communications

Task T5.2

Network topology

LTE-A Macro cells

UEs in and out of coverage

UE Relays to extend cell coverage



Environment Dense urban // Urban

Context Outdoor



Frequency bands LTE-A licensed bands


Backhaul NA

Propagation/channel model Official 3GPP channel models for D2D (when available)32

Mobility model Variable mix of static slow and high mobility users

Traffic model Periodic traffic, variable rate (10Hz, 5Hz, 2Hz, 1Hz, < 1Hz)

Small to medium packet size (up to 300 bytes)

Services Voice: %100 of users

Data: %100 of users


NA

KPIs, metrics involved Cell throughput, cell edge throughput


Efficient radio resource allocation schemes fitting all situations (inter-cell communication, in and without cell coverage, extension with UE relay)

Control channel for optimal D2D unicast communications

Evaluation method System level analysis




2.6.2 D2D broadcast communications

This scenario addresses various challenges associated with D2D broadcast communications:

• definition of an optimal waveform • Enabling the spatial reuse of resources dedicated to D2D communications by other D2D

transmissions • Optimization of UE relays using eMBMS technology for broadcast communications with out-of

coverage UEs UEs broadcast messages to other UEs that can be within the same cell (area controlled by a single eNodeB) or in different cells (areas possibly controlled by multiple eNodeBs).

Local services (D2D communications that take place within an area smaller than a cell) are locally announced by the service announcer and take place on a local set of resources that can be reused.

Services within a whole cell area (D2D communications that take place within an area controlled by the same eNodeB) can be announced and receive dedicated resources directly from the eNodeB.

Services on a wide area (D2D communications between users in areas controlled by multiple eNodeBs) will require a wide area resource allocation. These resources will be allocated by the service announcer in the specific area of interest.

These services could also work in case of temporary loss of eNodeB coverage and UE relay feature can give services to UEs which are out of coverage





Title D2D broadcast communications

Task T 5.2

Network topology LTE macro cells

Radio Access Technology (-ies) LTE / LTE-A


Environment Urban/Highway

Context Outdoor (for traffic safety applications/proximity safety service)

Inter-site distance Optimally: inter-site should lead to UEs to always be under coverage of 1 eNB (Macro)




Backhaul EPC should coordinate resource allocations between eNBs similarly to eMBMS (note: no synchronization of transmitted data is required)

Propagation/channel model Official 3GPP channel models for D2D (when available)33

Mobility model Variable mix of static slow and high mobility users

Traffic model Periodic traffic, variable rate (10Hz, 5Hz, 2Hz, 1Hz, < 1Hz)

Small to medium packet size (up to 300 bytes)

Services

Traffic safety applications exploiting Vehicle-to-Vehicle LTE Direct broadcast communications (proximity safety services). Application to ITS (Intelligent Transportation Systems) and Smart Cities.


NA


- Aggregated throughput

- Successful packet delivery rate

- Transmission range

- Fairness

- Delay


1) Maximize number of supported broadcast UEs with a given MAC-level probability of successful reception (collision avoidance)

− Adapting TX rate (periodicity),

− Adapting TX power

2) Given a UEs distribution in space, maximize the transmission range given a fixed pool of resources dedicated to D2D broadcast transmissions

Evaluation method Analytic

MAC level simulation/emulation




2.6.3 D2D communications for overloaded networks

This scenario will consist of a central loaded cell surrounded by a group of clusters of D2D enabled UEs. In each cluster there is a pair of D2D UEs wishing to communicate with each other. The eNB has no spare spectrum dedicated to D2D communications and so it must manage a spectrum reuse strategy to allow D2D communications and guarantee the QoS of all cell-center and cell UEs at the same time. The eNB spectrum management strategy will use/share the uplink spectrum as the spectrum is less loaded in uplink than in downlink (ongoing 3GPP discussions on the approval of such a spectrum sharing in the context of D2D study item for the upcoming release). The goal of eNB D2D management is to maximize the cell throughput gain by introducing D2D communications while preserving the QoS of all cell UEs (cell-center as well as cell edge).



Title D2D communications for overloaded networks

Task T5.2

Network topology Macro cell


Nature of small cells LTE D2D


Context Outdoor

Inter-site distance 500-1000 meters for macro-to-macro

Frequency deployment strategy Co-channel deployment.



Backhaul Ex: Ideal OR non-ideal channel gain beedback

Propagation/channel model Shadowing: log-normal distribution


Mobility model users at 3 km/hr

Traffic model Hotspot traffic and Traffic constant

Services Voice and Data


1x1

KPIs, metrics involved Avg. cell throughput


Maximizing cell throughput through intelligent interference management and resource allocation for spectrum sharing between cell users and D2D users.

Evaluation method Link level simulations and single cell

System level simulations




2.7 Deployment scenario 7: Carrier Aggregation (CA)

Deployment scenario 7 gathers the 4 following scenarios involving carrier aggregation:

1. Carrier aggregation using reconfigurable RF front-ends 2. Antenna Design: small antenna for femto cell, compatible with 4G systems bandwidths

3. Coordinated carrier aggregation in campus of femto base stations 4. Multi-Flow Carrier Aggregation

2.7.1 Carrier aggregation using reconfigurable RF front-ends

Within this scenario, a reconfigurable RF front-end is proposed to implement carrier aggregation. Using this technique the bandwidth can be extended up to 100MHz by aggregating up to five component carriers. CA appears as a key enabler in 3GPP Release 10 to support wider transmission bandwidth than 20MHz specified in 3GPP Release 8/9. As a result, it can improve network efficiency and user performance by dynamically allocating traffic across the entire available spectrum. This freedom to combine up to five carriers provides a reconfigurable and flexible bandwidth in order to support the demanded data (video, message, e-mail, phone calls, etc).

In order to implement CA, specific RF front-ends are required to support up to five carriers in the same band, identified as intra-band CA, or different bands, identified as inter-band CA. The study will be based on the design of reconfigurable RF front-ends to support CA, evaluating the RF requirements and constraints.

Various potential CA deployment scenarios have been described in 3GPP34. In Figure 24 , there are some examples that could be applied in macro cells or small cells. There are two possible cells, F1 and F2, working on the same base station. In the first example (a), F1 and F2 cells are co-located and overlaid, providing nearly the same coverage. A likely scenario is when F1 and F2 are of the same band. Therefore RF front-end should support intra-band CA. It is expected that aggregation is possible between overlaid F1 and F2 cells. In the second example (b), F1 and F2 cells could also be co-located and complementary where F2 is directed to the cell boundaries of F1 so that the cell edge throughput is increased. Then it is expected that F1 and F2 cells of the same eNB can be aggregated where coverage overlaps. A likely scenario is when F1 and F2 are of different bands. In this deployment, RF front-end should support inter-band CA.

a)

b)

Figure 24: CA deployment scenario a) providing nearly the same coverage and b) improving cell edge throughput

3GPP also describes CA deployment scenarios for HetNets (Figure 25). In the first example (a), F1 provides macro coverage and F2 is used to improve throughput at hotspots, offloading the macro cell. Mobility is performed based on F1 coverage and a likely scenario is when F1 and F2 are of different bands. A more challenging CA deployment scenario is presented in the second example (b), because the same frequency F2 is used by the base station and the hotspots so some limitations are required due to the inter-cell interference.



a)

b)

Figure 25: CA deployment scenario for HetNets

To be able to perform all these CA deployment scenarios, a reconfigurable RF front-end is an appropriate solution to adapt the communication to the different configurations.



Title Carrier aggregation using reconfigurable RF front-ends

Task T3.4

Network topology Macro cells // Small cells // HetNet (overlay of macro cells and small cells)


Nature of small cells LTE-A pico / femto

Environment Dense urban // Urban

Context Outdoor


Frequency deployment strategy Carrier aggregation deployment

Frequency bands LTE-A licensed bands


Backhaul




Propagation/channel model Path loss: Okumura-Hata or Veh-A etc.

Mobility model Static



Data: 100% of users


NA

KPIs, metrics involved Cell throughput, cell edge throughput


Provide reconfigurable RF front-ends to implement different CA deployment scenarios and improve cell throughput, cell edge throughput.

Evaluation method System level analysis

Links with other work packages: WP7 where a hardware development on carrier aggregation will be done



2.7.2 Antenna Design: small antenna for femto-cell, compatible with 4G

systems bandwidths

Similar to what has been described in section 2.7.1, an antenna is needed that adapts to the various CA

configurations. The challenge posed by such a broadband antenna relies in coping with the lowest frequencies of the band: indeed LTE-A has been allowed the 790-862MHz band. In addition to that, we want to forecast the use of TV White Space (TVWS) bands, i.e. 698-790MHz by future systems. At such low frequencies reducing the size of the antenna is a challenge, all the more so if the transmission bandwidth is large. Therefore, frequency agility will be considered at the lowest frequency bands.

The antenna will be connected to the RF developed in section 2.7.1, and measured in an anechoic

chamber. A prototype will be developed in WP7.



Title Antenna Design: small antenna for femto-cell, compatible with 4G bandwidths

Task T3.4

Network topology Small cell only


Nature of small cells LTE femto

Environment Offices , Homes/flats etc…

Context Indoor


Frequency deployment strategy Carrier aggregation

Frequency bands LTE licensed bands,TV whitespaces


Backhaul NA

Propagation/channel model 3GPP SCME35

Mobility model NA

Traffic model NA

Services NA


1

KPIs, metrics involved Radiation pattern.


Small antenna able to cope with the lowest frequency bands of LTE

Evaluation method Anechoic chamber measurements


Celtic-Plus SHARING

Public distribution

2.7.3 Coordinated carrier aggregation in campus of femto base stations

In this scenario we will evaluate a techniqUEs by the means of coordinated multiis receiving in this case datas and/or control from multiple base stations that coordinate theitransmission such as not to use the same transparent operation for the UE and reduce the interference arising from coordinated multipoint transmission.

The deployment scenario considered for thithat are deployed eventually under the coverage of single macro base station.

This scenario is applied also for the distributedIn this case we will have a campus of femto base stations that may be embedded into a network of 19 tri-sector macro base stations we have presented in

The femto campus scenario and the coordinated carrier aggregation are fbelow

Figure

Document D.2.2

Coordinated carrier aggregation in campus of femto base stations

In this scenario we will evaluate a technique to improve the throughput of carrier aggregation capable s by the means of coordinated multipoint transmission (CoMP). The carrier aggregation capable UE

is receiving in this case datas and/or control from multiple base stations that coordinate theitransmission such as not to use the same resources for the transmission to the UE. This will ensure transparent operation for the UE and reduce the interference arising from coordinated multipoint

The deployment scenario considered for this study is a dense group (campus) of femto base stations that are deployed eventually under the coverage of single macro base station.

distributed synchronization study that will be proposedwe will have a campus of femto base stations that may be embedded into a network of 19

sector macro base stations we have presented in section 0.

The femto campus scenario and the coordinated carrier aggregation are further described in the figure

Figure 26: Coordinated carrier aggregation

Document D.2.2

page 72 of 115

Coordinated carrier aggregation in campus of femto base stations

ue to improve the throughput of carrier aggregation capable point transmission (CoMP). The carrier aggregation capable UE

is receiving in this case datas and/or control from multiple base stations that coordinate their the transmission to the UE. This will ensure

transparent operation for the UE and reduce the interference arising from coordinated multipoint

s study is a dense group (campus) of femto base stations

will be proposed in Task 4.4. we will have a campus of femto base stations that may be embedded into a network of 19

urther described in the figure



Details of the femto campus simulation scenario is given by the table below


Title Coordinated carrier aggregation in campus of femto base stations

Task T4.4 Network topology Coordinated group of femto base stations (campus) Radio Access Technology (-ies) LTE-A Nature of small cells LTE-A Pico

Environment Dense urban Context Outdoor Inter-site distance Random deployment of the campus Frequency deployment strategy Co-channel deployment Frequency bands LTE licensed bands Density of small cells Dense/highly dense campus Backhaul ADSL non ideal backhaul

Propagation/channel model Path loss: outdoor femto model of TR36.81436 Shadowing: Fixed Fast fading: none

Mobility model Static to low mobility UEs Traffic model Spatial Poisson distribution with higher intensity around

the pico cells (as in 3GPP TR 36.81437). KPIs, metrics involved Cell Edge Throughput, Cell Edge SINR, Cell Center

Throughput, Cell Center SINR Description of the problem to be solved (target)

Coordinated carrier aggregation in campus of femto base stations.


Links with other work packages: The scenario is used in T4.4 as baseline for the evaluation of the coordinated carrier aggregation. The scenario have link with WP6 since it may lead to signaling over X2 and new coordination node for finding the CCs and PCCs.



2.7.4 Multi-Flow Carrier Aggregation

We look at the problem of single vs. multi-flow carrier aggregation in which users receive different flows coming from different tiers. The challenge lies in judiciously selecting flows and tiers on different component carriers along with suitable power levels. The considered baseline is the single flow when users receive one flow from the same tier. Different tradeoffs will be identified as well as design guidelines as to what mode is preferred and under which network conditions.



Title Multiflow carrier aggregation

Task T4.4

Network topology HetNet (macro + outdoor pico)



Environment Urban



Frequency deployment strategy Carrier aggregation



Backhaul X2









1x1 (downlink)




Total network power consumption (per hour)

kW/km2

Daily energy consumption (accumulated over 24 hours)


Single flow vs. multi flow carrier aggregation optimization problem as a function of traffic load, interference levels.





2.8 Deployment scenario 8: HSPA

Deployment scenario 8 gathers the following 2 scenarios involving HSPA communications:

1. Macro densification for system capacity/performance 2. Deployment optimization of Heterogeneous networks in urban/suburban environments

2.8.1 Macro densification for system capacity/performance

This scenario will be used to evaluate the performance of a densified HSPA macro network. The obtained results will be compared to the performance of the different kinds of heterogeneous HSPA network deployments.

The system simulations are assumed to be dynamic where user generation, user mobility, traffic model and RRM is assumed to interact dynamically and are modelled at least on the TTI level.

Main assumptions and simulation guidelines should be based in 3GPP case 1 (3GPP TR 36.814, v9.0.0).

The mobility signaling must be considered in this scenario and a call drop model based on the failure to deliver critical signal should be in place as well as the signaling bearer prioritization with respect to user plane data.

The details of this scenario are described in the table below.


Title Macro densification for system capacity/performance

Task T4.1

Network topology Macro only

Radio Access Technology (-ies) HSPA


Environment Urban or sub-urban

Context Outdoor areas

Inter-site distance 500-150 meters (urban) 1732-150 meters (sub-urban)

Frequency deployment strategy Single carrier co-channel deployment

Frequency bands UMTS licensed frequency bands


Backhaul Non-ideal (Iub)

Propagation/channel model 3GPP case 1 (3GPP TR 36.814, v9.0.0), no LOS component with shadow fading.

Mobility model Straight random direction movement.

Speed: 3, 30, 60, 90, 120 km/h

Traffic model 3GPP FTP model 1 with packet size equal to 500 kBytes and several intensities

Services 100% data (FTP)


No MIMO configured


Average throughput for the worst 5th percentile of the users

Offered area traffic, served area traffic


User drop rate

Noise rise level


Increase the total system performance/capacity by macro densification





2.8.2 Deployment optimization of Heterogeneous networks in urban/suburban

environments

This scenario will be used to estimate the performance of different types of heterogeneous HSPA network deployments.

The system simulations are assumed to be dynamic where user generation, user mobility, traffic model and RRM is assumed to interact dynamically and are modeled at least on the TTI level. Main assumptions and simulation guidelines should be based on 3GPP case 1 (3GPP TR 36.814, v9.0.0).

The low-power nodes can be located according to hotspot traffic or uniform random distributed locations within given binding conditions. A low-power node (LPN) can create a cell by its own or be part of a combined cell scheme, where the LPN would be recognized by the UE as one of several transmission (and/or reception) points of one specific cell. For example, a macro cell might contain several LPNs that act as distributed remote radio units (RRU) for the main macro unit, and operate with lower power compared to the macro cell. Also a LPN could form its own cell with at least one carrier in the same frequency than the macro layer (co-channel deployment).

The mobility signaling must be considered in this scenario and a call drop model based on the failure to deliver critical signal should be in place as well as the signaling bearer prioritization with respect to user plane data.



The details of this scenario are described in the table below:


Title Deployment optimization of Heterogeneous networks in urban/suburban environments

Task T4.1

Network topology HetNet (macro + outdoor low-power node)

Radio Access Technology (-ies) HSPA

Nature of small cells HSPA low-power node

Environment Urban and sub-urban

Context Outdoor only


Irregular for the LPN layer:

• Hotspot deployment (within traffic clusters).

• Random deployment (75m minimum distance between LPN and macro cell, 40m minimum distance between LPNs)

Frequency deployment strategy Single carrier Co-channel deployment

Frequency bands UMTS licensed bands

Density of small cells Sparse to dense (1-16 LPNs per macro)

Backhaul Non-ideal Iub and ideal RRU interface

Propagation/channel model 3GPP case 1 (3GPP TR 36.814, v9.0.0), no LOS component with shadow fading.

Mobility model Straight random direction movement.

Speed: 3, 30, 60, 90, 120 km/h

Traffic model

3GPP FTP model 1 with packet size equal to 500 kBytes

Voip traffic (90 sec. conversation, 50% activity and 12.2 kbps codec)

Mobile broadband traffic from real network traces as a background load.

User generation with several intensities.

Services 100% data (FTP) or 100% VoIP


No MIMO configured


Average throughput for the worst 5th percentile of the users

Offered area traffic, served area traffic


User drop rate

Noise rise level


Configure the optimal type of Heterogeneous deployment according to traffic, environment and load characteristics of the system.





2.9 Deployment scenario 9: Generic multi-cell/Technology agnostic

Deployment scenario 9 gathers the following 14 technology agnostic scenarios involving a generic multi-cell deployment:

1. Conversion block 2. Energy efficiency strategies over different power levels in the power amplifier 3. Extension of Performance Evolution for Femto -> Small Cells 4. Secure communication with imperfect CSIT 5. Flexible interference management schemes for downlink communications 6. Flexible interference management schemes for downlink communications

7. Theoretical Performance Analysis of Heterogeneous Cellular Networks 8. Relay-aided interference mitigation

9. Asymptotic Performance of Hetnets under Dynamic Traffic 10. Theoretical analysis for linearity and power efficiency trade-off in transmitters base stations 11. Distributed RRM Strategies without information exchange between the transmitters 12. Joint processing CoMP with limited backhaul and spatial CSIT allocation design

13. Interference alignment for large dense network 14. Broadcast Channel Feedback in Cooperated Multiple Antenna Systems

2.9.1 Conversion block

This scenario is set to enable managing the intra-LTE offloading via the definition and implementation of a middleware decision module. This module will be able to interact with the existing user and spectrum databases, obtaining from the needed information so as to allocate always a given resource to the best place. All RATs will be listed and categorized depending on their current status, and new ones can be added. Users will be managed, and the system will respond to a user petition with the best possible RAT available nearby (Figure 27).

The middleware will be based on IP and above protocols, so the network layout, frequency, mobility etc. of the underlying deployment is transparent.

One important assumption for the method is that is must maintain a desired level of QoS. It cannot be compromised by the changes triggered by this adaptation layer.

Transparent exchange of information must be assured, enabling fast and simple addition of new entities and making all information available to any of them. This is guaranteed by enabling a publisher/subscriber architecture.

Finally, the design will be done in line with IEEE802.21 standard requirements.

(a)

Entity Database

LTE eNodeBWiFi Access

Point

WiFi Entity

registering

(1) User

Registering

(2) Info request

about available

RATs

(4) User attached

to WiFi cell

(3) WiFi cell best

option



(b)

Figure 27: (a) System layout, (b) Conversion block details



Title Conversion block

Task T4.1 & T4.2

Network topology

NA

Radio Access Technology (-ies)

Nature of small cells

Environment

Context

Inter-site distance

Frequency deployment strategy

Frequency bands


Backhaul


Mobility model

Traffic model

Services


KPIs, metrics involved Overhead and delay introduced by the adaptation layer


Offloading and load balancing strategies (eNBs <--> small cells) to improve resource utilization. Managing the Intra-LTE and Inter-RAT offloading via the definition and implementation of a middleware.

Evaluation method Software simulation

Links with other work packages: As it is a mechanism envisaged to be deployed at IP and above layers, it does not imply modifications on the actual behavior of the current LTE network. All entities involved in the study should include support to implement this algorithm at software level. Therefore, no other WP interactions are foreseen.



2.9.2 Energy efficiency strategies over different power levels in the power

amplifier

This scenario presents a technique to adapt the bias point of the power amplifier according to the traffic load and output power requested, optimizing the power consumption in this stage. This solution could provide significant energy savings in the base station radio equipment, because the power amplifier is one of the most power consuming components.

During a day, the power consumption in a base station is related to the traffic load. Normally, the power amplifier is designed to support a maximum traffic load in the coverage area and provides the highest energy efficiency at maximum RF output power, i.e. at maximum traffic load. Nevertheless, when the traffic load decreases, lower RF output power levels are required and the energy efficiency becomes worse due to the PA power consumption characteristics. For that reason, the base station wastes energy in medium and low load situations. An innovative solution proposes a technique to dynamically adapt the power level in the PA to improve the energy efficiency in a base station. The adjustment is done by modifying the operating point in the PA using a dynamic supply voltage to adapt the RF output power level.

This technique is applied at hardware level, therefore several scenarios could be supported. The energy efficiency improvement will be significant at low traffic load because the required RF output power level decreases and the PA could reduce its output power level to satisfy traffic load demand.



Title Energy efficiency strategies over different power levels in the power amplifier

Task T4.3

Network topology Macro cell // Small cell (activity at hardware level)

Radio Access Technology (-ies) LTE-A and LTE


Environment Dense urban // Urban // Suburban // Rural

Context Outdoor

Inter-site distance Dynamic

Frequency deployment strategy NA

Frequency bands LTE/LTE-A licensed bands

Density of small cells Dense OR Sparse

Backhaul NA





Data: 100% of users


NA

KPIs, metrics involved Energy efficiency, throughput, average output power


Adjust the RF operation point in base stations to traffic load, optimizing the power consumption for a requested output power. Study focuses on power amplifier design.

Evaluation method Circuit level analysis




2.9.3 Extension of Performance Evolution for Femto -> Small Cells

In this scenario the aim is to explore the utilization of smart phones for monitoring, symbiosis, and performance evaluation in small cells networks, and the evolution of these, e.g. hetnets. This could be e.g. smart phones for location verifications, or explore the possibility and the opportunity to save energy in mobile networks with smart phones, or to increase capacity in mobile networks when users are indirectly guided to optimize their terminal locations



Title Extension of Performance Evolution for Femto -> Small Cells

Task T.6.2

Network topology

Macro cell/

Small cell evolutions and

HetNet (overlay of macro cells and small cells)

Radio Access Technology (-ies) LTE-A or HSPA

WiFi

Nature of small cells LTE or HSPA pico/micro/femto

WiFi

Environment Business District

Context Feedback evaluation of Smartphone's as a complementive performance management and monitoring in hetnet networks



Frequency bands LTE or HSPA licensed bands

WiFi unlicensed bands


Backhaul NA


Path loss: Okumura-Hata (parameters)

Shadowing: lognormal with exponential spatial correlation


Mobility model NA

Traffic model NA

Services NA


NA

KPIs, metrics involved Smart phones and Smart phone api's


Study the utilization of smartphones in performance evolution in HetNets and the impact on planning and optimization processes.

In conjunction with current sources of information e.g.

• Drive test data, OSS performance data

• As complementing source of information

• In conjunction with new monitoring capabilities offered by HetNets

Evaluation method

Agile method where evaluations are done with few selected use cases. Currently, use cases are the following:

• Utilization of smartphones measurements in verification of localizations architecture concepts

• Utilization of smart phones for user and network oriented symbiosis e.g. guidance of user based on performance evaluation




2.9.4 Secure Communication with Imperfect CSIT

This scenario aims at investigating the performance of a broadcast or interference channel with secrecy constraint, i.e. the message for one user should be kept secret to the others. The channel state information is assumed to be imperfect at the transmitter’s side. We would like to investigate the loss of throughput due to these additional constraints. More importantly, the efficient schemes in this context are expected to be quite different from the conventional ones without the constraints.



Title Secure communication with imperfect CSIT

Task T3.1

Network topology Cellular network with single or multiple cells

Radio Access Technology (-ies) NA


Environment NA

Context NA



Frequency bands NA


Backhaul NA

Propagation/channel model Path loss: To be determined

Fast fading: Rayleigh model


Traffic model NA

Services NA


General parameter

KPIs, metrics involved Degrees of freedom region, capacity


Characterization of the inner and outer bounds on the degrees of freedom region of this model and identification of the optimal transmission strategy, with the use of information-theoretic tools

Evaluation method Analytical/theoretical




2.9.5 Flexible interference management schemes for downlink communications

This scenario aims at investigating PHY-layer abstraction algorithms for closed-loop MIMO systems with limited feedback under the assumption of turbo processing at the destination (iterative (turbo) LMMSE-IC based receivers and variants). The objective is to evaluate the accuracy of existing Link to System interfaces (L2S) and design new L2S interface adapted to MCS sets based on more powerful codes (e.g., turbo codes or LDPC codes), coding across antennas (STBICM), and spatial precoding (e.g., antenna selection).



Title Flexible interference management schemes for downlink communications

Task T3.3

Network topology macro cells or small cells



Environment General

Context NA




Density of small cells low

Backhaul Ideal


Shadowing: lognormal

Path loss: any model

Fast fading: Rayleigh or Markov model

Mobility model static

Traffic model constant

Services NA


General parameter

KPIs, metrics involved Outage, prob of errors, average cell throughput


Cross-layer design and optimization for LTE and beyond, based on the assumption of turbo processing at the destination (iterative joint LMMSE-IC and decoding), flexible interference management concept

Evaluation method Semi Analytical




2.9.6 Theoretical Performance Analysis of Heterogeneous Cellular Networks

This scenario aims to analyze the performance of Heterogeneous networks, in terms of key measures such as coverage probability and area spectral efficiency, by taking into account the network geometry and the spatial randomness. The main focus will be to characterize the interference statistics and dynamics and to quantify the performance gains. Advanced PHY technologies (e.g. MIMO, cooperative transmission, interference alignment, etc - related to WP3 or even biasing, cell expansion, etc) will be analyzed and their effect on the network performance will be investigated. Backhaul models will be considered in our framework in order to provide a more complete characterization of network performance.



Title Performance analysis of Heterogeneous Cellular Networks

Task T4.1


Radio Access Technology (-ies) LTE-A compliant

Nature of small cells The analysis will be for general parameters (adaptation of which results in pico, femto, macro, etc)


Context Outdoor and indoor


Frequency deployment strategy Co-channel deployment or cognitive/flexible/adaptive frequency allocation

Frequency bands NA

Density of small cells Full range of SC density (sparse to low)

Backhaul Wired and wireless backhaul (perfect and imperfect)

Propagation/channel model Rayleigh or General (Gamma) small scale fading

Distance dependent pathloss attenuation

Mobility model Low to no mobility

Traffic model Constant

Services NA


General parameter

KPIs, metrics involved Coverage probability, area spectral efficiency (throughput), average spatial rate


- Quantify the interference statistics and characterize the performance taking into account spatial randomness and uncoordinated interference.

- Propose mechanisms to enhance the performance (coverage, rate)

Evaluation method Analytical/Theoretical




2.9.7 Relay-Aided Interference Mitigation

This scenario aims at investigating the performance of an interference channel with a relay. More precisely, the objective is to provide an analysis of the capacity region of such a channel model in the context of a cellular network. Even though this study is theoretical, it provides an insight on the roles of a relay for interference mitigation and the kinds of efficient strategies to be used for different channel conditions.



Title Relay-aided interference mitigation

Task T3.3

Network topology Cellular network with infrastructure relay


Nature of small cells General parameters

Environment NA

Context NA



Frequency bands NA


Backhaul NA

Propagation/channel model Path loss: To be determined

Fast fading: Rayleigh model


Traffic model NA

Services NA


General parameter

KPIs, metrics involved Capacity region


Characterization of the inner and outer bounds on the capacity region of this network, with the use of information-theoretic tools





2.9.8 Asymptotic Performance of HetNets under Dynamic Traffic

This scenario aims at investigating the impact of bursty traffic on the performance of HetNets. More precisely, the objective is to provide an asymptotic performance analysis in the context of heavy traffic, i.e. when the traffic arrival rates are pushed very close to the capacity region boundary. This scenario allows an easier analysis (and better understanding) of the delay and queue stability/overflow due to the phenomenon of state space collapse. Even though this study is theoretical, it allows designing advanced stable resource allocation strategies that ensure queue/delay outage constraints and therefore stringent QoS constraints.



Title Asymptotic Performance of HetNets under dynamic traffic

Task T4.1




Environment NA

Context NA



Frequency bands NA

Density of small cells Low

Backhaul Ideal

Propagation/channel model Path loss: tbd

Fast fading: Rayleigh or Markov model with finite number of states


Traffic model Dynamic traffic arrival (i.i.d.)

Services NA


General parameter

KPIs, metrics involved Queue stability, Avg. cell throughput


Find asymptotic limits that capture the dynamic evolution of the queue length of each transmitter and define strategies that minimizes the queue outage/delay





2.9.9 Theoretical analysis for linearity and power efficiency trade-off in

transmitters base stations

Power amplifiers consume most of the power of macro base stations transmitters. Power amplifiers have low efficiencies in their linear areas and high efficiencies close to their saturations, i.e. in nonlinear areas. Some recent communication standard set up a peak factor reduction technique (to enhance the power amplifier efficiency) followed by a linearization (to linearize the power amplifier characteristic). So in such a scenario, the performance of the linearization is influenced undoubtedly by the peak factor reduction method. In this contribution, we study the Error Vector Metric (EVM) metric and evaluate a closed form regarding the performance of both the peak factor reduction technique and the linearization. We choose the pre-distortion as a linearization technique and define a pre-distortion error. Assuming that the baseband OFDM signal is characterized as a complex Gaussian process we will first study the distribution of the resulted signal after peak factor reduction. Then, we will derive theoretical expressions of the first and second order moments of the pre-distortion error and show that the error depends mainly on the peak factor of the signal after peak factor reduction method and on the pre-distortion quality. This expected result is a trade-off between linearization and crest factor performance. The details of this scenario are described in the table below:


Title Energy efficiency enhancement strategies in macro cells power amplifiers

Task T4.3

Network topology Macro cell

Radio Access Technology (-ies) LTE-A and LTE


Environment Dense urban // Urban // Suburban // Rural

Context Outdoor



Frequency bands LTE/LTE-A licensed bands


Backhaul NA


Mobility model NA

Traffic model NA

Services NA


NA


Power amplifier efficiency

Interference level

Error Vector Magnitude


Trade-off between linearity and power amplifier efficiency. Joint crest factor and linearity approach at power amplifier level





2.9.10 Distributed RRM Strategies without information exchange between the

transmitters

This scenario aims at developing advanced distributed RRM strategies that ensure the stability of the network (in terms of queue stability) in the context of dynamic traffic arrival. The transmitters (for instance base stations, access points, etc.) do not exchange any information between each other.

Furthermore, we will develop advanced learning-based RRM frameworks that allow users to learn and improve their network utilities (rate, QoS, QoE, probability of success, etc.) in HetNets based on local information and noisy measurements.



Title Distributed RRM strategies

Task T4.4

Network topology Any (multi-cell, Ad-Hoc, etc.)

Radio Access Technology (-ies) Generic


Environment NA

Context NA



Frequency bands NA

Density of small cells Low

Backhaul Ideal

Propagation/channel model Path loss: to be determined

Fast fading: Rayleigh or Markov model with finite number of states


Traffic model Dynamic traffic arrival (i.i.d.)

Services


General parameter

KPIs, metrics involved Outage prob, Avg. cell throughput


Develop distributed RRM strategies that minimize the outage or maximize a network utility function (QoS, QoE)





2.9.11 Joint processing CoMP with limited backhaul and spatial CSIT allocation

design

In this scenario, we will consider a K-cell network operating in the same frequency band, with one single-antenna transmitter (TX) and one single-antenna receiver (RX) in each cell. The TXs share users’ data perfectly so as to jointly serve the RXs. The channel state information (CSI) estimate is first fed back from RX to TX in each cell, and further shared among TXs via limited-capacity backhaul links, such that each TX obtains its own estimate of the global multi-user channel, which is different from one another. This CSI configuration is referred to as distributed CSIT setting. With distributed CSIT, two problems are under investigation: 1) analyzing the performance of JP-CoMP transmission with regard to the quality of CSI shared among TXs; 2) designing the spatial CSIT allocation policies among TXs for joint cooperation.



Title Joint processing CoMP with limited backhaul and spatial CSIT allocation design

Task T3.1

Network topology Multi-cell

Radio Access Technology (-ies) LTE-A and/or 5G


Environment NA

Context Outdoor


Frequency deployment strategy Full reuse

Frequency bands NA


Backhaul Non-ideal backhaul between cells

Propagation/channel model Fast fading model + standard distance dependent path loss model


Traffic model NA

Services NA


Arbitrary

KPIs, metrics involved Sum rate and/or Degrees of freedom


Performance analysis of joint processing CoMP with regard to the quality of CSI shared among transmitters;

Designing spatial CSIT allocation policies through distance-based approach

Evaluation method Analytical and simulation (MATLAB)




2.9.12 Interference alignment for large dense network

In this scenario, we study a large dense cellular network modeled by a K-user MIMO interference channel (IC), where data sharing is not allowed among transmitters (TXs). Instead of global channel state information (CSI) sharing among TXs as considered in conventional interference alignment (IA) model, only incomplete CSIT (i.e., a sub-matrix of the global channel matrix) is available. We aim at designing the CSI allocation policies to preserve IA feasibility while reducing the amount of CSI sharing required at the TXs.



Title Interference alignment for large dense network

Task T3.3

Network topology Multi-cell

Radio Access Technology (-ies) LTE-A and/or 5G


Environment NA

Context Outdoor


Frequency deployment strategy Co-channel

Frequency bands NA


Backhaul Non-ideal backhaul between cells

Propagation/channel model Fast fading model + distance-dependant path loss model

Mobility model NA

Traffic model NA

Services NA


Arbitrary

KPIs, metrics involved Sum rate or Degrees of freedom


CSI allocation policies design preserving interference alignment feasibility while reducing the amount of CSI sharing required at the transmitters

Evaluation method Analytical and simulation (MATLAB)




2.9.13 Broadcast Channel Feedback in Cooperated Multiple Antenna Systems

This scenario evaluates the performance of broadcast feedback in a large dense network.

Via this scenario we study the impact of imperfection in the CSI feedback for multi-cell network MIMO. The objective is also to find the optimum feedback scheme and optimum cluster of base stations that are participating in the CoMP for any set of users.

System level simulations will be carried out extensively to study the scenario. The numerical results of the simulations will be compared with analytical expressions to get more insights into the scenario.



Title Broadcast Channel Feedback in Cooperated Multiple Antenna Systems

Task T3.1

Network topology Multi cell


Nature of small cells Pico cells


Context Both Outdoor and Indoor UEs

Inter-site distance 500 m ISD for macro cells

Variable for small cells ISD



Density of small cells Variable

Backhaul Both ideal (i.e. typical point to point fiber) and non-ideal backhauls will be considered


Path loss: Generic exponential

Shadowing: Fixed


Mobility model Pedestrian (3 km/h)


Services High speed data.


2X1, 4X1

KPIs, metrics involved Sum Rate


Impact of limited feedback and feedback design for CoMP

Evaluation method Simulations and analytical analysis




3 Definition of KPIs

Key Performance Indicators (KPI) can be defined as a set of metrics that allow a reliable and complete assessment of network performance in the evaluated scenario. Key performance indicators allow a conclusive comparison of a set of solutions.

The objective of this section is to make a state-of-the art of the existing relevant KPIs and to complement these already existing KPIs with the ones that drive the project innovations toward the desired directions. Since the objective of the project is to move towards a ubiquitous quality of experience, KPIs such as the ratio of the cell-average over the cell-edge spectrum efficiency normalized with respect to the number of users (Jain index40) could serve as an interesting basis for improving the existing KPIs. In addition to this, efforts could be made in order to measure the energy efficiency of the techniques that aim at fulfilling this objective of throughput homogeneity over the cell, while classical green metrics measure the energy efficiency of the increase of throughput. These metrics use power and traffic models for the calculation of the consumed power. Models already designed for the Base Stations by previous projects like EARTH41 or consortiums like Greentouch42 could be reused.

Investigations will also be made in order to provide a new definition of the capacity that takes into account operator deployment constraints. The capacity could be defined as the maximum cell throughput assuming that a certain criterion is respected for all users, such as a minimum throughput per user.

3.1 KPIs state-of-the-art

Whenever several partners want to cooperate in developing advanced systems, a common understanding of the metrics is necessary in order to compare the results in a well-defined manner. To achieve this, different organizations have proposed a fixed set of evaluation metrics or key performance indicators43. In this section the most relevant existing approaches will be presented in order to derive a set of appropriate metrics for the SHARING project, taking into account several different existing classifications.

The existing indicators can be classified according to their high-level functionally (above/beyond technical) as follows:

• Performance related KPIs: Metrics that allow evaluation of the proposed solutions in terms of network and user perceived performance. Some examples are:

o Spectral efficiency (SE) – measure of performance of a system in relation to the utilized frequency bandwidth.

o Reliability – measure of the quality of a radio link connection for fulfilling a certain service level.

o Fairness – measure of performance uniformity within network. o Latency – measure of time delay experienced in a system. It can be defined either as

one-way or round-trip transmission time. o Traffic volume density - traffic volume density describes the total user data volume

transferred to/from end-user devices during a predefined time span divided by the area size covered by the radio nodes belonging to the RAN(s)

o Availability and retainability – measures the percentage of users or communication links for which the QoE requirements are fulfilled within a certain geographical area

o Quality of Experience (QoE) – measure of user's experience for a given service. This is a more subjective measure that by replacing traditional QoS measures like throughput and delay with appropriate utility functions tries to capture the user perceived end-to-end experience.

o Quality of Service (QoS) – measure of performance for the provided service, based on measurable quantities.

• Complexity related KPIs: Metrics that allow evaluation of the proposed solutions in terms of resources needed for their application. The related KPIs should consider such factors as:

o Amount of required control information exchanged between nodes and between nodes and core network elements,

o Processing effort – measure of computational complexity of the proposed algorithms. This should include e.g. number of required parameters, measurements etc.

o Energy consumption of a network. o Power consumption related to the processing of different modules forming the PHY layer.

• Economic related KPIs: Metrics that allow the evaluation of investments and operating expenses



o Cost (CAPEX and OPEX): The infrastructure part is typically divided into the capital investment to acquire and deploy the network, called Capital Expenditures (CAPEX), and the costs to operate the network, called operational expenditure (OPEX).

The next subsections present the existing KPIs, classified according to their technical functionality. The classification is based on the scale of the technical evaluation, which is also linked to the protocol layers, ranging from small-scale (link-level) to medium- to large-scale (system-level) evaluations.

3.1.1 System-level KPIs

The traffic model in system-level evaluations is known to have a great impact on the performance results. Typically, full-buffer, TUDR or more realistic traffic models that take into account specific service types can be considered.

For evaluations with full-buffer traffic model (data traffic), the following performance metrics are suggested to be considered:

• Mean user throughput. • Throughput CDF (Cumulative Distribution Function). • Median and 5% worst user throughput.

For evaluations with bursty traffic model (data traffic), the following performance metrics need to be considered:

• User perceived throughput (during active time), defined as the size of a burst divided by the time between the arrival of the first packet of a burst and the reception of the last packet of the burst

o Average perceived throughput of a user defined as the average from all perceived throughput for all bursts intended for this user

o Tail perceived throughput defined as the worst 5% perceived throughput among all bursts intended for a user.

• User perceived throughput CDF (average and/or tail user perceived throughput) • Percentage of users with 1% or more dropped packets. • Median and 5% worst user perceived throughput (average and/or tail user perceived

throughput). • Overall average user throughput defined as average over all users perceived throughput. • Time variations of the throughput in a given scenario and associated traffic and power models.

VoIP capacity evaluations necessitate the following performance metrics to be considered:

• VoIP system capacity in form of the maximum number of satisfied users supported per cell in downlink and uplink.

• System capacity is defined as the number of users in the cell when more than 95% of the users are satisfied.

Metrics44 for HetNet scenarios should also be considered, since the main focus of SHARING is HetNets. Those metrics can include the conventional KPIs (e.g. Jain Index measuring fairness) as well as HetNet specific KPIs on scalability and deployment aspects such as:

o base station density to provide a given coverage with a given quality in a given area should be evaluated

o the impact of using relay instead (or in addition) to base station will be assessed

Several additional KPIs have been defined for evaluation of HetNets in previous European projects such as Artist4G or WINNER. These are:

• Throughput CDFs for macro UEs and for UEs served by low power nodes (relays). • Macro cell area throughput (mean and CDF). • Fraction of total throughput over low power nodes (relays). • Macro and low power node (relay) serving UE throughput ratio.

Similar metrics are also mentioned by other organizations such as Next Generation Mobile Networks (NGMN) or ITU-R (Radiocommunication). The NGMN Project is an initiative by a group of leading operators to provide a vision for technology beyond 3G.

Additionally, the NGMN evaluation guideline defines a fairness measure and addresses different delay aspects. Therefore, only the metrics not covered by 3GPP are mentioned here. The metrics proposed there are listed here with only short descriptions.

• Fairness: The normalized throughput bound defines a region in the throughput cumulative density function plot.



• Latency o Connection setup latency: connection setup latency and the corresponding cumulative

density function, guaranteed connection setup latency o Radio access transmission latency : one way radio access network transmission latency,

the corresponding cumulative density function and 95%ile, radio access network jitter o End-to-end packet call latency: cumulative density function and 95%ile of the end-to-

end packet call latency. o Handover interruption time : interruption time during a handover of real-time and non-

real-time services within an NGMN system and between an NGMN system and a different RAN

o Overhead: Control channel and pilot overhead: percentage of radio resources utilized for signaling, control channels and pilots

The Radiocommunication Sector of the International Telecommunication Union (ITU-R) also defined a set of requirements for future mobile communication systems45. The metrics defined there are mainly covered by those mentioned before. A wide range of KPIs have been defined in the WINNER II project46. In addition to the standard KPIs as throughput or data rates WINNER II defined metrics considering the resource management and deployment aspects:

• Packet Flow Establishment Time • Radio resource management related criteria:

o session rejection rate for new session o session drop rate for on-going session o trade-off between frequency reuse pattern and system capacity

• Complexity, costs, power consumption

In ITU-T Rec. P.80047 procedures for the evaluation of the speech quality are provided. The following KPI is defined (and will be used within certain scenarios of the project):

• The Mean Opinion Score (MOS): o It provides a numerical indication of perceived quality, expressed as a number in the

range 1 (poor quality) to 5 (excellent). Test procedures for the evaluation of MOS basically consist in a set of standard, subjective tests where a pool of listeners in controlled scenarios rate the audio quality of certain test sentences.

o Even if subjective testing is the most proper method of measuring voice quality; methods for measuring MOS objectively are also available, as for example the E-model originally developed within ETSI and then standardized by the ITU

For advanced relaying studies, simulation results can be analyzed and compared with the other concepts using the following system level KPIs:

• The exchange ratio between macro eNBs and relays that provide the same performance in terms of coverage (see also section 6.2.3 on the methodology). Then, knowing the cost ratio between macro eNB and relays it is possible to derive if relay deployment provides cost savings compared to macro eNB-only deployment.

• The exchange ratio between pico and relay that provides the same performance in terms of average throughput (see also section 6.2.3 on the methodology). Then, knowing the cost ratio between pico and relays it is possible to derive if relay deployment is a low cost solution compared to pico deployment.

• Packet delay: The definition of the user plane latency in 4.1.1 can be extended to further measurement points in the transmission chain. This would offer a better view on how the delay is affected by the introduction of the relay node and how the latter would affect the overall delay.

o End-2-end delay: Measured between the application source and sink. o Detailed split of air interface delay: Measured separately for Un and Uu interface, always

from sending to receiving PDCP entity. o Same as the u-plane latency, these additional KPIs should be computed as

� Mean value (across time and/or data flows). � Jitter (i.e. variance, across time and/or data flows). � CDF (per QoS flow class).

• Buffer size: o Will be computed as

� Mean value (across time and/or DRB) � CDF (per QoS class).

o Possible sub-KPIs are



� Buffer size in the User equipment (UE) for uplink Uu DRBs � Buffer size in the relay node (RN) for downlink Uu or uplink Un DRBs � Buffer size in the (D)eNB for downlink Uu and Un DRBs.

• Signaling overhead on the Un interface: o Were computed as

� Mean value (across time). � CDF.

o Possible sub-KPIs are � u-plane overhead: � c-plane overhead

o Relates to the amount of resources that have to be used in order to support the novel relaying concepts

• Resource consumption: o Were computed as

� Mean value (across time per data flow) � CDF (per data flow)

o Possible sub-KPIs are � Number of PRBs occupied per active data flow on DL and UL. � Tx power used by each UE and the RN on the UL.

3.1.2 Link level KPIs

The metrics described in the previous section are intended mainly for system level evaluation. In order to decrease simulation effort, the behaviour of link level is often simulated separately and then modelled for system level evaluations. For the link level simulations different KPIs have to be defined. A widely used list of common criteria was derived within the WINNER II project to compare simulation results:

• Bit error rate / Frame error rate o RawBER: Bit Error Rate. For raw performance after the demodulation stage. o BER: Bit Error Rate at the output of the channel coding decoder. o CWER: Code Word Error Rate. At the output of the channel coding decoder o BLER: Block Error Rate. For intermediate decoding of HARQ blocs. It should anyhow be

specified especially in case of incremental redundancy cases. o FER: Frame Error Rate. The frame represents the information block protected by cyclic

redundancy check (CRC) at the radio link control (RLC) layer. It could be a code word; in that case it is equivalent to CWER.

o IPER: IP Packet Error Rate. The error probability of an entire IP packet. • User throughput. • Delay

3.1.3 Field trials KPIs

Various KPIs stated in previous sections, in particular those connected to fairness aspects, are difficult to measure in field trials due to the limited number of UEs/eNBs involved in a measurement campaign. Hence, it appears suitable to define specific field trial KPIs which can then be mapped onto the system level KPIs defined before (e.g. through improved system level simulation48):

• BLER / BLER improvements due to the usage of ARTIST4G techniques • SINR / SINR improvements due to the usage of ARTIST4G techniques • Throughput / throughput improvements due to the usage of ARTIST4G techniques

3.1.4 Misalignments and issues

In this section, possible problems, issues or misalignments concerning the KPIs proposed in the previous state of the art section are examined. Among those considered are:

• Lack of or inadequate KPIs in order to assess whether the system requirements are fulfilled. • Lack of KPIs to assess the user Quality of Experience (QoE). • Lack of KPIs for the assessment of new performance parameters (e.g., energy efficiency).

In the context of the ARTIST4G49 project, new and refined KPIs have been proposed:

• Single User Performance Metrics

Single user performance metrics are the most basic ones used to characterize the network. They may be defined for single cell and multi cell and provide, by means of the link budget, an estimation of the cell range and the supported bit rate for a single user. In the single cell evaluation, the link



budget provides the noise limited range and bit rate for the system, whilst in the multi-cell evaluation, the performance indicators correspond to the interference limited case. The main issue with these KPIs is that cell range/coverage area are determined based on link budget inputs corresponding to data rates, cell-area reliability etc., and no ITU requirements are specified for these link budget inputs. So there is a degree of uncertainty that may lead to non-comparable results. For example, the shadowing fade margin in the link budget is determined as a function of the cell-edge coverage reliability and the standard deviation of the log-normal shadow fading (including penetration losses if applicable). The cell-edge coverage reliability is determined for the given area coverage reliability as a function of the shadow fading standard deviation and the path-loss exponent obtained from the path-loss model, and it can be determined using simulations or using traditional numerical methods. However, for this parameter, there are not agreed values for the standard deviation of the log-normal shadow fading nor for the path-loss exponent to be used in different environments.

• Capacity related KPIs

Some of the most important KPIs defined in the previous section are related to the capacity estimations associated with the implementation of different techniques. One important issue concerning capacity related KPIs is the possible misalignment between the estimated KPI and the perceived QoE by the user. This is an area that has been addressed by both NGMN and 3GPP in order to provide more meaningful KPIs, especially for bursty traffic conditions, as those indicated in the previous sections. However, there may be different additional problems:

o Distortions due to TCP flow control mechanisms, like slow-start. Reduced latency may have an impact on window sizes and other parameters.

o Latency due to signaling procedures when changing of radio resource control (RRC) state. The delay may have a significant impact on the perceived bit rate by the user when a new connection should be established if the volume of data to be sent is small and the bit rate supported is large.

Another aspect that should be taken into account is the support of heterogeneous deployment environments, with macro, micro and femto cells, alongside with advanced relays. These KPIs are being proposed in the context of the project.

• Mobility related KPIs

Mobility KPIs are usually related to the rate handovers fail or succeed, as well as the interruption time due to the completion of the process and the probability of losing data. They are much more significant for real time services, like voice and some video services. Mobility KPIs are difficult to define in a way that reflects QoE estimation, as the latter will depend on different factors like the mobility degree of the user, the characteristics of the network that provides coverage (e.g., hierarchical networks with macro/micro/femto cells), etc. For example, a handover failure rate may provide an acceptable or unacceptable QoE depending on the characteristics of the network. Also mobility is one of the aspects that is more difficult to simulate in a realistic way in system level simulations. Other mobility related procedures, like system selection or tracking area update, are also complicated to assess. For example, evaluating tracking area update success rate is complicated by the fact that tracking areas can be reconfigured in order to optimize paging/update signaling trade-off.

• Performance uniformity KPIs

It is becoming more and more important to improve the performance of users that are in worse propagation and interference conditions, providing a more uniform experience over the whole coverage area. Different possibilities are available: ratio between the maximum bit rate supported and cell edge bit rate, ratio between cell edge bit rate with and without load, percentage of coverage points that exceed a given bit rate threshold, etc. In ARTIST 4G, the use of the Jain Index was proposed for the characterization of the performance uniformity (variability is the term used for characterizing this performance metric).

In real networks, evaluating the performance uniformity would require to distinguish between those cell edge users that are limited (in terms of performance) by coverage and those that are limited by interference. The latter can be improved by means of interference avoidance/exploitation innovations, whilst the former would require mechanisms to improve their sensitivity (e.g., improving the SINR).

• Energy saving KPIs

Technical improvements, reflected in better KPIs, should not come at the expense of excessive energy consumption either by the UE or by the network. In this sense, it would be interesting to rank the proposed solutions in terms of their energy requirements. This is an area that has not been



addressed yet. It remains an open issue whether significant results can be obtained from system level simulations for such KPIs.

• SON related KPIs

The standardized set of capabilities known as Self-Organizing Networks (SON) will significantly increase the level of automation in operations and maintenance and decrease the associated OPEX. SON is defined as a set of use cases that cover the entire network lifecycle (planning, deployment, operations, and optimization) and it is being designed to be a multi-vendor solution, with standard interfaces utilized at key points to allow inter-operability between equipment vendors. Some SON algorithms are not standardized in order to allow for differentiation and competition between the vendors infrastructure.

In principle, it could be possible to use system level simulations in order to assess the effectiveness of some SON procedures, especially those intended to improve spectral efficiency and capacity by changing the network configuration. However, in order to obtain relevant performance metrics it should be taken into account that the temporal dynamics of SON are not the same ones as used in system level simulations for the evaluation of, e.g., radio resource management (RRM) procedures.

• Complexity related KPIs

The evaluation of the technical innovations that are proposed should not be limited to the performance they provide, but also to the complexity they introduce. Complexity in this context is understood as a compendium of different factors, like the processing capabilities required for supporting a given innovation by both the network and the UE, the additional control and signaling capabilities to be provided, or any other requirements in terms of infrastructure deployment. Complexity thus becomes a metric of the technical feasibility of an innovation.

Complexity KPIs are difficult to provide until the innovation analyzed is mature enough to design a possible implementation; however some qualitative assessment may be provided, for example, comparing with a baseline system.

• Cost related KPIs

The feasibility of any technical innovation to be incorporated in the standards will depend on the economic viability of its implementation in real networks. In this sense, it would be desirable to introduce some metrics that help to relate the introduction of new features with their associated cost increase (or decrease). Obviously it is practically impossible to perform such evaluation in absolute terms, but it could be possible to provide estimations of relative cost with respect to a baseline configuration.

Obviously, cost and complexity are quite intertwined, as a higher complexity usually implies a higher equipment cost, although also a lower cost per bit (due to higher efficiency).

Considering that interference management is one of the key issues addressed in SHARING, in the majority of the studied cases, the following parameters need to be mentioned:

• The interference power: The average total power of the interferences disturbing that User. • The interference load: the average number of simultaneous interferers over all resource

elements scheduled for that user. • The interference diversity: the total (cumulative) number of different interferers that have been

scheduled on all the resource elements of this user. • The interference intelligibility: for instance, the average of the difference between the

modulation orders of the signal intended to the user and the interfering signals. If the intelligibility is positive, it means that most of the interfering signals are easily detectable, whereas a negative intelligibility indicates that most interfering signals are barely detectable.

These parameters are calculated on resource elements assigned to users. They help characterize the interference environment in which the proposed algorithms are tested.

Note: in the above definitions an interferer is to be understood as the signal intended to an interfering user. Thus the number of interferers is the number of interfering streams, and not the number of interfering base stations. This last set of interference parameters, when associated with corresponding sets of ranges, will form a set of interference scenarios. This can be done when first results of the projects provide the first values for these parameters.



3.2 Selection of KPIs for SHARING

After a thorough review of the proposed scenarios and the expected results, we anticipate that the relevant KPIs the SHARING project take into account the following aspects:

• User traffic (multiple services, service usage, user spatial 3D distribution, time variability).

• Requirements and solutions for a realistic channel modeling, considering heterogeneous cell

configurations, multiple frequencies and frequency bands, multi-antenna systems

• HetNet deployment scenarios

• The ratio of the cell-average over the cell-edge spectrum efficiency normalized to the same number of users (Jain index)

The table presented below lists all the scenarios foreseen within the SHARING project and for each of

these scenarios, a list of KPIs involved is provided.

Table 1: Summary of scenarios, tasks, partners and KPIs involved

Scenario Title Task Partner KPIs involved

Joint processing CoMP algorithm,

combined with interference

cancellation at the receiver.

3.1 CEA Sum rate, Bit Error Rate, Avg. cell

throughput

Cross-layer performance evaluation

of multi-cell cooperative schemes in


3.1 FT Block Call Rate, Cell Loads, File

Transfer Time, Throughput, Capacity.

Secure communication with imperfect

CSIT 3.1 SUP Capacity, sum rate

Multipoint Coordination Schemes for

LTE-Advanced Networks 3.1 FT Avg. cell throughput

Joint processing CoMP with limited

backhaul and spatial CSIT allocation

design

3.1 EUR Sum rate and/or Degrees of freedom

Interference reduction in LTE through

link level processing 3.1/3.2 TCS & SEQ Bit Error Rate

Broadcast Channel Feedback in

Cooperated Multiple Antenna Systems 3.1 FT & EUR Sum Rate

Joint interference and location

prediction 3.3-1 CEA Interference estimation

Antenna Design: small antenna for

femto-cell, compatible with 4G

systems bandwidths

3.3-2 CEA & TTI Radiation pattern.

Interference alignment for large

dense network 3.3 EUR Sum rate OR Degrees of freedom

Flexible interference management

schemes for downlink

communications

3.3 SUP Outage, prob of errors, average cell

throughput

Relay-aided interference mitigation 3.3 SUP Capacity region

Carrier aggregation using

reconfigurable RF front-ends 3.4 TTI Cell throughput, cell edge throughput

LTE-A multi-layer network in

urban/suburban environments

3.1,

3.2, 3.3 SIRADEL

Service coverage, spectral efficiency,

throughput, energy efficiency, …





considering non-ideal backhaul

4.1 SIRADEL Service coverage, spectral efficiency,


Performance and energy efficiency

evaluation in LTE Heterogeneous

Networks

4.1 FT Average cell throughput and cell-edge

(5% percentile)

Mobility load balancing (MLB) in LTE

macro cell networks 4.1 FT

Block Call Rate, Drop Call Rate, Load,

File Transfer Time, Cell Edge

Throughput, Cell Center Throughput

Load Balancing (LB) via transmit

power optimization in LTE macro cell 4.1 FT

Block Call Rate, Load, File Transfer

Time

HetNet mobility 4.1 ERICSSON

System coverage, System

performance, average throughput,

Handover failure rate

Macro densification for system

capacity/performance 4.1 ERICSSON

System coverage, System capacity,

user performance, User drop rate,

Noise Rise level

Deployment optimization of

Heterogeneous networks in


4.1 ERICSSON

System coverage, System capacity,

user performance, User drop rate,

Noise Rise level

Capacity optimization through Active

Antenna Systems (AAS) in LTE macro

cell networks

4.1 FT

Block Call Rate, Cell Edge Throughput,

Cell Center Throughput, Load, File

Transfer Time for both Target and

Observational cells

Dynamic UL/DL duplexing for TDD

enabled small cells 4.1 UOULU

Average throughput per user - DL and

UL packet throughputs

Theoretical Performance analysis of

Heterogeneous Cellular Networks 4.1 SUP

Coverage probability, area spectral

efficiency (throughput), average

spatial rate

Asymptotic Performance of Hetnets

under dynamic traffic 4.1 SUP Queue stability, Avg. cell throughput

Conversion block 4.1 &

4.2

TTI &

ASYSOL

Overhead and delay introduced by the

adaptation layer

Seamless Offloading in

Heterogeneous Wireless Networks 4.2 AVEA

Client SNR (dB), PSNR (dB), Data

received (Kbps), Packet Loss (%),

Throughput (Kbps), MOS Value (Mean

Opinion Score)

Enhanced InterCell Interference Co-

ordination (eICIC) for interference


4.2 FT

Block Call Rate, Cell Edge Throughput,

Cell Edge SINR, Cell Center

Throughput, Cell Center SINR

Antenna tilt optimization for

interference management in LTE-A

HetNets

4.2 FT

Block Call Rate, Pico and Macro Cell

Edge Throughput, Cell Edge SINR, Pico

and Macro Cell Center Throughput, Cell

Center SINR

LTE-WiFi offloading 4.2 UOULU

Average throughput per user (-> worst

5th percentile of users -> coverage),

Offered area traffic or served area

traffic (Mbps/km2, GB/h/km2 ->

capacity)



Dynamic Cell ON/OFF power saving 4.3 TTI Avg. cell throughput, %savings, µJ/bit

(if power models available)

Multi-RAT Heterogeneous networks in


4.2,

4.3, 4.4 SIRADEL

Service coverage, spectral efficiency,


ON/OFF power saving in campus of

small cells 4.3 MERCE

Cell Edge Throughput, Cell Edge SINR,

Cell Center Throughput, Cell Center

SINR

Power saving through sum power

optimization in HetNet deployment 4.3 MERCE



SINR

HetNet energy saving via eNodeB

sleep mode/cell switch-off 4.3 ERICSSON


5th percentile of users -> coverage)



capacity)

Total network power consumption (per

hour) kW/km2 Daily energy

consumption (accumulated over 24

hours)

Opportunistic ON/OFF switching for

small cells 4.3 UOULU


5th percentile of users -> coverage)



capacity), Total network power

consumption (per hour)

kW/km2, Daily energy consumption

(accumulated over 24 hours)

Energy efficiency strategies over

different power levels in the power

amplifier

4.3 TTI Energy efficiency, throughput, average

output power

Multiflow carrier aggregation 4.4 UOULU





capacity), Total network power

consumption (per hour)

kW/km2, Daily energy consumption

(accumulated over 24 hours)

Backhaul offloading via Opportunistic

caching 4.4 UOULU





capacity), Average backhaul load

saving

Coordinated carrier aggregation in

campus of femto base stations 4.4 MERCE



SINR

Distributed RRM strategies 4.4 SUP Outage prob, Avg. cell throughput

Joint channel and Network coding for

advanced networks 5.1 CEA Bit Error Rate



Clustered wireless mesh networks

based on LTE 5.1 EUR & TCS

For simulation: Packet Error Rate, Avg.

cell/user throughput, Avg. user delay,

Avg. fairness index, Min throughput,

Max delay, Max drop rate

For demonstration: Achievable

throughput/delay/BLER on relayed CH-

CH link, end-to-end

throughput/delay/BLER (core network

to user)

LTE-Advanced collaborative relaying 5.1 EUR

Achievable throughput on relayed eNB-

UE link, end-to-end throughput (core

network to user)

Path loss model for relay scenario 5.1 FT Path loss and theoretical throughput

D2D Unicast communications 5.2 NTUK Cell throughput, cell edge throughput

D2D broadcast communications 5.2 EUR

- Aggregated throughput

- Successful packet delivery rate

- Transmission range

- Fairness

- Delay

D2D communications for overloaded

networks 5.2 FT Avg. cell throughput

Antenna Smart Grid Solutions For

Outdoor DAS 6.2 ECE Capacity and topology comparison

Extension of Performance Evolution

for Femto -> Small Cells 6.2 ECE Smart phones and Smart phone api's

Positioning in Heterogeneous

converged Networks 6.2 MAGISTER

Primary KPIs: Cumulative distributions

of positioning error over the area of

interest, 68%-ile and 95%-ile

prediction accuracies over the area of

interest, Prediction accuracy vs.

confidence in a particular location.



4 Definition of the evaluation methodology

Based on the defined set of requirements, the objective will be to define the evaluation methodology for all the research directions that will be investigated in the project. This definition incorporates channel models, deployments scenarios and simulation tools. Based on these metrics, the project objectives will be quantified for each specific scenario in order to enable the evaluation of the project progress.

On many research topics, performance evaluation metrics and evaluation methodologies are well established based on the results from standardization bodies (3GPP, NGMN, ITU, IEEE) and previous research projects (WINNER50, EASY-C51). In these cases, to maximize the impact of the project and minimize unnecessary efforts, the project only focuses on a reduced number of deployment scenarios and parameters selected from the existing methodologies. Working on such a common reduced set of deployment scenarios will guarantee that different partners and work packages in the project obtain simulation results whose trends can be compared. On the contrary, for topics that have not been deeply investigated in the past, WP2 will aim at establishing a consensus in the industry and scientific community on simple and representative metrics and evaluation methodologies. Multi radio access technologies networks with LTE and WiFi are one of the topics that will require such contributions.

In the following table, the different evaluation methodologies proposed for each scenarios ranging from simulation at different levels (MAC, Link, flow and system), analysis of circuits, relying on theory and statistics are synthesized.

The most frequently proposed evaluation methodology is the simulation at the system level with multi-cell configurations. Simulation at the link level is also proposed, less frequently and MAC level simulation is proposed in two scenarios only.

More extensively, evaluation methodologies proposed are:

• System level simulation • Link level simulations • MAC level simulations • Circuit level analysis • Experiments with OpenAirInterface • Use of Cross-layer models based on queuing theory. • Coverage-based analysis • Software simulation • Flow level simulations • Analytical/Theoretical • SON techniques



Table 2: Summary of evaluation methodology for each scenario

Scenario Title Task Partner Evaluation methodology

CoMP algorithm, combined with

interference cancellation at the

receiver.

3.1 CEA System level simulations

Cross-layer performance evaluation

of multi-cell cooperative schemes in


3.1 FT Cross-layer models based on queuing

theory and system-level simulations.

Multipoint Coordination Schemes for

LTE-Advanced Networks 3.1 FT System level simulations

Secure communication with imperfect

CSIT 3.1 SUP Analytical analysis.

Broadcast Channel Feedback in

Cooperated Multiple Antenna

Systems

3.1 FT & EUR Simulations and analytical analysis

Interference reduction in LTE through

link level processing 3.2 TCS & SEQ Analytical and Link level simulations

Joint interference and location

prediction 3.3 CEA System level

Antenna Design: small antenna for

femto-cell, compatible with 4G

systems bandwidths

3.4 CEA & TTI Measurements in anechoic chamber

Interference alignment for large

dense network 3.3 EUR Analytical and simulation (MATLAB)

Flexible interference management

schemes for downlink

communications

3.3 SUP Analytical and link-to-system level

evaluation

Relay-aided interference mitigation 3.3 SUP Analytical/theoretical

Carrier aggregation using

reconfigurable RF front-ends 3.4 TTI System level analysis



3.1-

3.3 SIRADEL

Coverage-based analysis + System-level

simulations



considering non-ideal backhaul

4.1 SIRADEL Coverage-based analysis + System-level

simulations

Performance and energy efficiency

evaluation in LTE Heterogeneous

Networks

4.1 FT System level simulations

Mobility load balancing 4.1 FT Dynamic multi-cell system level

simulation

Load Balancing via Transmit Power

Optimization in LTE macro cells 4.1 FT

Flow level simulations based on queuing

theory

HetNet mobility 4.1 ERICSSON dynamic multi-cell system level

simulations

Macro densification for system

capacity/performance 4.1 ERICSSON

Dynamic multi-cell system level

simulations



Deployment optimization of

Heterogeneous networks in


4.1 ERICSSON multi-cell system level simulations

Capacity optimization through Active

Antenna Systems (AAS) in LTE macro

cell networks


Joint processing CoMP with limited

backhaul and spatial CSIT allocation

design

3.1 EUR Analytical and simulation (MATLAB)

Dynamic UL/DL duplexing for TDD

enabled small cells 4.1 UOULU Analytical and Simulations (MATLAB)

Theoretical Performance analysis of

Heterogeneous Cellular Networks 4.1 SUP Analytical/Theoretical

Asymptotic Performance of Hetnets

under dynamic traffic 4.1-2 SUP Analytical/theoretical

Conversion block 4.1 &

4.2

TTI &

ASYSOL Software simulation

Seamless Offloading in

Heterogeneous Wireless Networks 4.2 AVEA Analytical AND System level simulations

Enhanced InterCell Interference Co-

ordination (eICIC) for interference





HetNets


LTE-WiFi offloading 4.2 UOULU System level simulations



HetNets


Dynamic Cell ON/OFF power saving 4.3 TTI

SON techniques for finding the

compensation small cells in the campus

of the base stations.

Multi-RAT Heterogeneous networks in


4.2-

4.4 SIRADEL

Coverage-based analysis + System-level

simulations

HetNet energy saving via eNodeB

sleep mode/cell switch-off 4.3 ERICSSON multi-cell system level simulations

Opportunistic ON/OFF switching for

small cells 4.3 UOULU Analytical and simulation (MATLAB)

ON/OFF power saving in campus of

small cells 4.3 MERCE System level simulations

Energy efficiency strategies over

different power levels in the power

amplifier

4.3 TTI Circuit level analysis

Power saving through sum power

optimization in HetNet deployment 4.3 MERCE System level simulations

Multiflow carrier aggregation 4.4 UOULU System level simulations

Backhaul offloading via Opportunistic

caching 4.4 UOULU

Analytical using synthetic and possibly

real traces.



Distributed RRM strategies 4.4 SUP Analytical

Coordinated carrier aggregation in

campus of femto base stations 4.4 MER System level simulations

Joint channel and Network coding for

advanced networks 5.1 CEA Link level

Clustered wireless mesh networks

based on LTE 5.1 EUR & TCS


MAC level simulations


LTE-Advanced collaborative relaying 5.1 EUR

Link level simulations,

System level simulations,


Path loss model for relay scenario 5.1 FT Measurement campaign and Shannon

capacity

D2D broadcast communications 5.2 EUR Analytic

MAC level simulation/emulation

D2D Unicast communications 5.2 NTUK System level analysis

D2D communications for overloaded

networks 5.2 FT

Link level simulations and single cell

System level simulations

Antenna Smart Grid Solutions For

Outdoor DAS 6.2 ECE System level simulations

Extension of Performance Evolution

for Femto -> Small Cells 6.2 ECE

Agile method where evaluations are done with few selected use cases. Currently, use cases are the following:

• Utilization of smartphones measurements in verification of localizations architecture concepts

- Utilization of smart phones for

user and network oriented

symbiosis e.g. guidance of user

based on performance

evaluation

Positioning in Heterogeneous

converged Networks 6.2 MAGISTER

System level simulations and

performance measurements in

LTE/WLAN environment.



5 Conclusions

In order to achieve the overall goal of the SHARING project, this deliverable has presented and described the deployment scenarios, the individual partner scenarios within these deployment scenarios, the relevant KPIs (Key Performance Indicators) and involved evaluation methodologies.

The document describes nine deployment scenarios covering LTE and HSPA technologies, macro cells, small cells and HetNets configurations, relay and D2D communications, carrier aggregation and inter-RAT systems, as well as those scenarios where the radio access technology and network type is transparent/generic:










These deployment scenarios will allow SHARING to evaluate and compare different deployment strategies for today’s and tomorrow’s radio access networks in terms of the identified KPIs through the proposed evaluation methodologies. Thus, we will be able to come up with recommendations to the operators on questions on which deployment strategy is expected to bring about how much gain in terms of several important KPIs, under which conditions, what are the drawbacks and architectural impact etc. This will be made possible by the use of the information presented in this deliverable by the other WPs of the project. More precisely, this deliverable will be used to define and refine the research/innovation which will be performed in the other WPs of the project. Besides, technical work in the other work packages of the SHARING project will be made easier by the early definition of scenario characteristics. KPIs and evaluation methodology are also defined at an early stage. KPIs will provide a measure of the success of SHARING.



6 List of Abbreviations, Acronyms, and Definitions

3GPP Third Generation Partnership Project

AAS Active Antenna Systems

ABS Almost Blank Sub-frame

ACTS Advanced Communications Technologies and Services

ADSL Asymmetric Digital Subscriber Line

AMC Adaptive Modulation and Coding

ANR Agence Nationale de la Recherche

AP Access Point

ARPU Average Revenue Per User

ASIC Application Specific Integrated Circuit

BAN Body Area Network

BBU Base Band Unit

BeFEMTO Broadband evolved FEMTO

BER Bit Error Rate

BRAN Broadband Radio Access Network

BS Base Station

BTS Base Transceiver Station

CA Carrier Aggregation

CAPEX Capital Expenditure

CC Component Carrier

CCIR Comité Consultatif International des Radiocommunications

CDF Cumulative Distribution Function

CDMA Code Division Multiplexing Access

CEPT Conférence Européenne des Postes et Télécommunications

CO Confidential

CoMP Coordinated Multi-Point

COST european Cooperation in Science and Technology

CPE Customer Premise Equipment

C-RAN Centralized Radio Access Network

CRC Cyclic Redundancy Check

CRS Common (or Cell specific) Reference Signal

CS Coordinated Scheduling

CSG Closed Subscriber Group

CSI Channel State Information

CSIT Channel State Information at Transmitter

CT Core network and Terminals

CTO Chief Technical Officer

CTU Chief Technical Officer

CWC Centre for Wireless Communications

D2D Device-to-Device

DARPA Defense Advanced Research Projects Agency

DAS Distributed Antenna System

DeNB Donor eNodeB

DL DownLink

DRX Discontinuous Reception



DSL Digital Subscriber Loop

DSTL Defense Science and Technology Laboratory

DTX Discontinuous Transmission

DVB Digital Video Broadcasting

E2E End-to-End

EARTH Energy Aware Radio and network tecHnologies

EC European Commission

eICIC Enhanced Inter-Cell Interference Cancellation

eNB Evolved NodeB

EPC Evolved Packet Core

EPON Ethernet Passive Optical Network

ES Energy Saving

ETSI European Telecommunications Standards Institute

EU European Union

E-UTRAN Evolved Universal Terrestrial Radio Access Network

FDD Frequency Division Duplex

FPGA Field Programmable Gate Array

FRN Fixed Relay Node

FTP File Transfer Protococl

GA General Assembly

Gbps Gigabit per second

Gbyte Gigabyte

GPRS General Packet Radio Service

GPS Global Positioning System

GSM Global System for Mobile

GSMA GSM Alliance

GW GateWay

HARQ Hybrid Automatic Repeat reQuest

HDR Habilitation à Diriger les Recherches

HeNB Home eNB

HF High Frequencies

HO Hand Over

HSDPA High Speed Downlink Packet Access

HSPA High Speed Packet Access

HW Hardware

IA Interference Alignment

IC Interference Cancellation

ICIC Inter-Cell Interference Cancellation

IEEE Institute of Electrical and Electronics Engineers

IMT International Mobile Telecommunications

IP Internet Protocol

IPR Intellectual Property Rights

ITU International Telecommunication Union

ITU-R International Telecommunication Union-Radio

JP Joint Processing

KPI Key Performance Indicator

L2S Link-to-System



LAN Local Area Network

LDPC Low Density Parity Check

LE Low Energy

LMMSE Linear Minimum Mean Squared Error

LPN Low Power Node

LTE Long Term Evolution

LTE-A Long Term Evolution - Advanced

M2M Machine-to-Machine

MAC Medium-Access Control

MADM Multiple Attribute Decision Making algorithm

MBB Mobile BroadBand

Mbps Megabit per second

Mbyte Megabyte

MC Multi Carrier

MIMO Multiple Input Multiple Output (MU-MIMO see MU)

MME Mobility Management Entity

MNO Mobile Network Operator

MRN Mobile Relay Node

MS Mobile Station

MTC Machine Type Communications

MU Multi-User

NA Not Applicable

NAS Network Access Server

NFC Near Field Communications

NGMN Next Generation Mobile Networks

OFDM Orthogonal Frequency Division Multiplexing

OFDMA OFDM Access

OPEX Operational Expenditure

OSS Operations Support System

OSTBC Orthogonal Space Time Block Code

PA Power Amplifier

PAPR Peak to Average Power Ratio

PC Personal Computer

PCC Primary Component Carrier

PDCP Packet Data Convergence Protocol

PDF Probability Density Function

PDR Packet Delivery Rate

PER Packet Error Rate

PHY Physical Layer

PM Project Manager

PSNR Peak Signal to Noise Ratio

PU Public

QMR Quarterly Management Report

QoE Quality of Experience

QoS Quality of Service

RAN Radio Access Network

RAT Radio Access Technology



RF Radio Frequency

RLC Radio Link Control

RN Relay Node

RNC Radio Network Controller

RRC Radio Resource Control

RRM Radio Resource Management

RTD Research and Technical Development

RTT Round Trip Time

RX Receiver

SC Single Carrier

SCME 3GPP Spatial Channel Model Extended

SER Symbol Error Rate

SINR Signal to Interference plus Noise Ratio

SISO Single Input Single Output

SME Small and Medium Entreprise

SMS Short Message Service

SNR Signal to Noise Ratio

SON Self Optimizing/Organizing Network

STBICM Space-Time Bit Interleaved Coded Modulation

SW Software

TA Tracking Area

Tbps Terabit per second

Tbyte Terabyte

TC Test Case

TCO Total Cost of Ownership

TCP Transmission Control Protocol

TD Time Division

TDD Time Division Duplex

TM Task Manager

TR Technical Requirement

TTI Transmission Time Interval

TUDR Typical User Data Rate

TX Transmitter

UE User Equipment

UK United Kingdom

UL Uplink

UMTS Universal Mobile Telecommunication System

UT User Terminal

UTRA Universal Terrestrial Radio Access

UTRAN Universal Terrestrial Access Network

UWB Ultra Wide Band

VNI Visual Networking Index

VoIP Voice over Internet Protocol

VPL Vehicle Penetration Loss

WCDMA Wideband Code Division Multiplexing Access

WiFi / Wi-Fi Wireless Fidelity

Wi-Fi Wireless Fidelity



WLAN Wireless Local Area Network

WP Work Package

WPL Work Package Leader



7 Annex: scenarios/partners

This annex indicates which SHARING partner is responsible for each scenario.

Scenario Name Partner

Deployment scenario 1: LTE macro cell only

2.1.1 Mobility Load Balancing (MLB) in LTE macro cell networks FT

2.1.2 Load Balancing (LB) via transmit power optimization in LTE macro cell networks

FT

2.1.3 Capacity optimization through Active Antenna Systems (AAS) in LTE macro cell networks

FT

2.1.4 Cross-layer performance evaluation of multi-cell cooperative schemes in LTE macro cell networks

FT

2.1.5 Interference reduction in LTE through link level processing TCS & SEQ

2.1.6 Multipoint Coordination Schemes for LTE-Advanced Networks FT

2.1.7 Joint Processing CoMP algorithm, combined with interference cancellation at the receiver

CEA

Deployment scenario 2: LTE small cells only

2.2.1 Dynamic UL/DL Duplexing for TDD-enabled Small Cells UOULU

2.2.2 ON/OFF power saving in campus of small cells MERCE

Deployment scenario 3: LTE HetNet (macro + micro/pico/femto)

2.3.1 Enhanced Inter-Cell Interference Co-ordination (eICIC) for interference management in LTE-A HetNets

FT

2.3.2 Antenna tilt optimization for interference management in LTE-A HetNets

FT

2.3.3 HetNet energy saving via eNodeB sleep mode/cell switch-off ERICSSON

2.3.4 Dynamic cell ON/OFF power saving TTI

2.3.5 HetNet mobility ERICSSON

2.3.6 LTE-A multi-layer network in urban/suburban environments SIRADEL

2.3.7 LTE-A multi-layer network in urban/suburban environments considering non-ideal backhaul

SIRADEL

2.3.8 Opportunistic ON/OFF Switching for small cells UOULU

2.3.9 Backhaul offloading via Opportunistic caching UOULU

2.3.10 Power saving through sum power optimization in HetNet deployments

MERCE

2.3.11 Antenna Smart Grid Solutions For Outdoor DAS ECE

2.3.12 Joint interference and location prediction CEA

2.3.13 Performance and energy efficiency evaluation in LTE Heterogeneous Networks

FT

Deployment scenario 4: Inter-RAT HetNet (LTE + WiFi)

2.4.1 Multi-RAT Heterogeneous networks in urban/suburban environments SIR

2.4.2 LTE-WiFi Offloading UOULU

2.4.3 Seamless Offloading in Heterogeneous Wireless Networks AVEA

2.4.4 Positioning in Heterogeneous Converged Networks MAGISTER

Deployment scenario 5: Relays

2.5.1 LTE-Advanced collaborative relaying EUR

2.5.2 Clustered wireless mesh networks based on LTE TCS & EUR

2.5.3 Joint channel and Network coding for advanced networks CEA

2.5.4 Path loss model for relay scenario FT

Deployment scenario 6: Device-to-Device (D2D)



2.6.1 D2D Unicast communications NTUK

2.6.2 D2D broadcast communications EUR

2.6.3 D2D communications for overloaded networks FT

Deployment scenario 7: Carrier Aggregation (CA)

2.7.1 Carrier aggregation using reconfigurable RF front-ends TTI

2.7.2 Antenna Design: small antenna for femto-cell, compatible with 4G systems bandwidths

CEA & TTI

2.7.3 Coordinated carrier aggregation in campus of femto base stations MERCE

2.7.4 Multi-Flow Carrier Aggregation UOULU

Deployment scenario 8: HSPA

2.8.1 Macro densification for system capacity/performance ERICSSON

2.8.2 Deployment optimization of Heterogeneous networks in urban/suburban environments

ERICSSON

Deployment scenario 9: Generic Deployment/Technology Agnostic

2.9.1 Conversion block TTI & ASYSOL

2.9.2 Energy efficiency strategies over different power levels in the power amplifier

TTI

2.9.3 Extension of Performance Evolution for Femto -> Small Cells ECE

2.9.4 Secure Communication with Imperfect CSIT SUP

2.9.5 Flexible interference management schemes for downlink communications

SUP

2.9.6 Theoretical Performance Analysis of Heterogeneous Cellular Networks

SUP

2.9.7 Relay-Aided Interference Mitigation SUP

2.9.8 Asymptotic Performance of HetNets under Dynamic Traffic SUP

2.9.9 Theoretical analysis for linearity and power efficiency trade-off in transmitters base stations

SUP

2.9.10 Distributed RRM Strategies without information exchange between the transmitters

SUP

2.9.11 Joint processing CoMP with limited backhaul and spatial CSIT allocation design

EUR

2.9.12 Interference alignment for large dense network EUR

2.9.13 Broadcast Channel Feedback in Cooperated Multiple Antenna Systems

FT & EUR



8 References

1 J. Ramiro and K. Hamied, Self-Organizing Networks (SON): Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE (Wiley, 2011) 2 T. Bonald and A. Proutiere, “Wireless downlink data channels: user performance and cell dimensioning,” in Proc. ACM Mobicom’03, SanDiego, CA, Sept. 20 3 T. Bonald and A. Proutiere, “Wireless downlink data channels: user performance and cell dimensioning,” in Proc. ACM Mobicom’03, SanDiego, CA, Sept. 2003 4 3GPP TR 36.814, “E-UTRA; Further advancements for E-UTRA physical layer aspects,” 3rd Generation Partnership Project (3GPP), 2010. 5 3GPP TS 36.331, “LTE; E-UTRA; Radio Resource Control; Protocol specification,” 3rd Generation Partnership Project (3GPP), 2012 6 3GPP TR36.887 study of energy saving enhancement in E-UTRAN 7 3GPP TR 36.814 - Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects ( v.0.4) 8 3GPP TR 36.814: Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects ( v.0.4) 9 ITU-R Recommendation P.1411-4, “Propagation data and prediction methods for the planning of short-range outdoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 100 GHz”. 10 Eraldo Damosso, Luis M. Correia (ed.), “Digital Mobile Radio Towards Future Generation Systems”, COST 231 Final Report 11 European Cooperation in the Field of Scientific and Technical Research, EURO-COST 231, “Urban Transmission Loss Models for Mobile Radio in the 900 and 1800 MHz Bands”, COST 231 TD (91) 73. Rev 2, The Hague, September 1991. 12 3GPP TR 36.814, “Evolved Terrestial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects (Release 9)”, V9.0.0, March 2010. 13 EARTH project : http://archive.eurescom.eu/activities/EU_Projects/earth.asp 14 3GPP TR 25.943, “Deployment aspects (Release 9)”, V9.0.0, December 2009 15 3GPP TR 25.814, “Physical layer aspect for evolved Universal Terrestrial Radio Access (E-UTRA)”, 3rd Generation Partnership Project (3GPP), 2006 16 3GPP TR 36.814, “E-UTRA; Further advancements for E-UTRA physical layer aspects,” 3rd Generation Partnership Project (3GPP), 2010 17 3GPP TR 25.814, “Physical layer aspect for evolved Universal Terrestrial Radio Access (E-UTRA)”, 3rd Generation Partnership Project (3GPP), 2006 18 3GPP TR 36.814, “E-UTRA; Further advancements for E-UTRA physical layer aspects,” 3rd Generation Partnership Project (3GPP), 2010 19 Consensus- based decentralization of interior point methods for heterogeneous networks energy saving. PIMRC 2011 page 300-304 20 Ram Rajagopal and Martin J. Wainwright, Network-Based Consensus Averaging With General Noisy Channels 21 3GPP TR 36.814: Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects 22 TR 25.814, TR 36.814 23 TS 36.839 24 TS 36.839 25 3GPP TR 25.814, “Physical layer aspect for evolved Universal Terrestrial Radio Access (E-UTRA)”, 3rd Generation Partnership Project (3GPP), 2006 26 3GPP TR 36.814, “E-UTRA; Further advancements for E-UTRA physical layer aspects,” 3rd Generation Partnership Project (3GPP), 2010 27 I. Basturk, B. Ozbek, C. Edemen, A. S. Tan, E. Zeydan and S. Ergut, “Radio resource management for OFDMA-based mobile relay enhanced heteregenous cellular networks " in 2013 IEEE 77th Vehicular Technology Conference (VTC 2013) , June 2013, Dresden, Germany



28 3GPP TR 25.814, “Physical layer aspect for evolved Universal Terrestrial Radio Access (E-UTRA)”, 3rd Generation Partnership Project (3GPP), 2006 29 3GPP TR 36.814, “E-UTRA; Further advancements for E-UTRA physical layer aspects,” 3rd Generation Partnership Project (3GPP), 2010 30 3GPP TR 36.839, “Evolved Universal Terrestrial Radio Access (E-UTRA); Mobility enhancements in heterogeneous networks”, 3rd Generation Partnership Project (3GPP), 2013 31 3GPP TR 36.839, “Evolved Universal Terrestrial Radio Access (E-UTRA); Mobility enhancements in heterogeneous networks”, 3rd Generation Partnership Project (3GPP), 2013 32 TR 36.843, section A.2.1.2. (But the TR is not yet approved and no implementation available yet) 33 TR 36.843, section A.2.1.2. (But the TR is not yet approved and no implementation available yet) 34 3GPP TS 36.300, “Overall description; Stage 2”, 3rd Generation Partnership Project (3GPP), 2011. 35 D. S. Baum, J. Hansen, G. Del Galdo, M. Milojevic, J. Salo and P.Kyösti, “An interim channel model for beyond-3G systems – Extending the 3GPP spatial channel model (SCM)”, in Proc. of IEEE VTC 2005 Spring, Stockholm 2005, pp. 3132 – 3136 36 3GPP TR 36.814: : Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects 37 3GPP TR 36.814: : Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects 38 3GPP TR 25.814, “Physical layer aspect for evolved Universal Terrestrial Radio Access (E-UTRA)”, 3rd Generation Partnership Project (3GPP), 2006 39 3GPP TR 36.814, “E-UTRA; Further advancements for E-UTRA physical layer aspects,” 3rd Generation Partnership Project (3GPP), 2010 40 http://www.ist-winner.org/, https://ict-artist4g.eu/ 41 https://www.ict-earth.eu/, http://www.greentouch.org/index.php?page=projects 42 https://www.ict-earth.eu/, http://www.greentouch.org/index.php?page=projects 43 http://www.greentouch.org/index.php?page=projects 44 http://www.greentouch.org/index.php?page=projects 45 IMT requirements for 4G systems 46 http://www.ist-winner.org/, https://ict-artist4g.eu/ 47 http://www.itu.int/rec/T-REC-P.800/en 48 https://ict-artist4g.eu/ 49 https://ict-artist4g.eu/ 50 http://www.ist-winner.org/ 51 http://www.easy-c.de/index_en.html

self-organized heterogeneous advanced … deliverable d2.2... · scenarios, kpis and evaluation...

Documents